Gene methylation in ovarian cancer diagnosis

ABSTRACT

The present invention provides DNA biomarker sequences that are differentially methylated in samples from normal individuals and individuals with ovarian cancer. The invention further provides methods of identifying differentially methylated DNA biomarker sequences and their use the detection and diagnosis of ovarian cancer.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

The present patent application claims benefit of priority to U.S.Provisional Patent Application No. 60/899,218, filed Feb. 2, 2007; U.S.Provisional Patent Application No. 60/970,322, filed Sep. 6, 2007; U.S.Provisional Patent Application No. 61/021,840, filed Jan. 17, 2008; U.S.Provisional Patent Application No. 60/899,137, filed Feb. 2, 2007; andU.S. Provisional Patent Application No. 60/968,690, filed Aug. 29, 2007,each of which are incorporated by reference.

BACKGROUND OF THE INVENTION

Human cancer cells typically contain somatically altered genomes,characterized by mutation, amplification, or deletion of critical genes.In addition, the DNA template from human cancer cells often displayssomatic changes in DNA methylation. See, e.g., E. R. Fearon, et al, Cell61:759 (1990); P. A. Jones, et al., Cancer Res. 46:461 (1986); R.Holliday, Science 238:163 (1987); A. De Bustros, et al., Proc. Natl.Acad. Sci. USA 85:5693 (1988); P. A. Jones, et al., Adv. Cancer Res.54:1 (1990); S. B. Baylin, et al., Cancer Cells 3:383 (1991); M. Makos,et al., Proc. Natl. Acad. Sci. USA 89:1929 (1992); N. Ohtani-Fujita, etal., Oncogene 8:1063 (1993).

DNA methylases transfer methyl groups from the universal methyl donorS-adenosyl methionine to specific sites on the DNA. Several biologicalfunctions have been attributed to the methylated bases in DNA. The mostestablished biological function is the protection of the DNA fromdigestion by cognate restriction enzymes. This restriction modificationphenomenon has, so far, been observed only in bacteria.

Mammalian cells, however, possess different methylases that exclusivelymethylate cytosine residues on the DNA that are 5′ neighbors of guanine(CpG). This methylation has been shown by several lines of evidence toplay a role in gene activity, cell differentiation, tumorigenesis,X-chromosome inactivation, genomic imprinting and other major biologicalprocesses (Razin, A., H., and Riggs, R. D. eds. in DNA MethylationBiochemistry and Biological Significance, Springer-Verlag, N.Y., 1984).

In eukaryotic cells, methylation of cytosine residues that areimmediately 5′ to a guanosine, occurs predominantly in CpG poor loci(Bird, A., Nature 321:209 (1986)). In contrast, discrete regions of CGdinucleotides called CpG islands (CGi) remain unmethylated in normalcells, except during X-chromosome inactivation and parental specificimprinting (Li, et al., Nature 366:362 (1993)) where methylation of 5′regulatory regions can lead to transcriptional repression. For example,de novo methylation of the Rb gene has been demonstrated in a smallfraction of retinoblastomas (Sakai, et al., Am. J. Hum. Genet., 48:880(1991)), and a more detailed analysis of the VHL gene showed aberrantmethylation in a subset of sporadic renal cell carcinomas (Herman, etal., Proc. Natl. Acad. Sci. U.S.A., 91:9700 (1994)). Expression of atumor suppressor gene can also be abolished by de novo DNA methylationof a normally unmethylated 5′ CpG island. See, e.g., Issa, et al.,Nature Genet. 7:536 (1994); Merlo, et al., Nature Med. 1:686 (1995);Herman, et al., Cancer Res., 56:722 (1996); Graff, et al., Cancer Res.,55:5195 (1995); Herman, et al., Cancer Res. 55:4525 (1995).

Identification of the earliest genetic and epigenetic changes intumorigenesis is a major focus in molecular cancer research. Diagnosticapproaches based on identification of these changes can allowimplementation of early detection strategies, tumor staging and noveltherapeutic approaches targeting these early changes, leading to moreeffective cancer treatment. The present invention addresses these andother problems.

BRIEF SUMMARY OF THE INVENTION

The present invention provides methods for determining the methylationstatus of an individual. In one aspect, the methods comprise:

-   -   obtaining a biological sample from an individual; and    -   determining the methylation status of at least one cytosine        within a DNA region in a sample from an individual where the DNA        region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%,        or 99% identical to, or comprises, a sequence selected from the        group consisting of SEQ ID NO.: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485.

In a further aspect, the methods comprise determining the presence orabsence of cancer, including but not limited to, bladder, breast,cervical, colon, endometrial, esophageal, head and neck, liver, lung,melanoma, ovarian, prostate, renal, and thyroid cancer, in anindividual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without cancer,        wherein the comparison of the methylation status to the        threshold value is predictive of the presence or absence of        cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without bladder        cancer, wherein the comparison of the methylation status to the        threshold value is predictive of the presence or absence of        bladder cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without breast        cancer, wherein the comparison of the methylation status to the        threshold value is predictive of the presence or absence of        breast cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without        cervical cancer, wherein the comparison of the methylation        status to the threshold value is predictive of the presence or        absence of cervical cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without colon        cancer, wherein the comparison of the methylation status to the        threshold value is predictive of the presence or absence of        colon cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without        endometrial cancer, wherein the comparison of the methylation        status to the threshold value is predictive of the presence or        absence of endometrial cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without        esophageal cancer, wherein the comparison of the methylation        status to the threshold value is predictive of the presence or        absence of esophageal cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without head        and neck cancer, wherein the comparison of the methylation        status to the threshold value is predictive of the presence or        absence of head and neck cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without liver        cancer, wherein the comparison of the methylation status to the        threshold value is predictive of the presence or absence of        liver cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without lung        cancer, wherein the comparison of the methylation status to the        threshold value is predictive of the presence or absence of lung        cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without        melanoma, wherein the comparison of the methylation status to        the threshold value is predictive of the presence or absence of        melanoma in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without ovarian        cancer, wherein the comparison of the methylation status to the        threshold value is predictive of the presence or absence of        ovarian cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without        prostate cancer, wherein the comparison of the methylation        status to the threshold value is predictive of the presence or        absence of prostate cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without renal        cancer, wherein the comparison of the methylation status to the        threshold value is predictive of the presence or absence of        renal cancer in the individual.

In some embodiments, the methods comprise:

-   -   a) determining the methylation status of at least one cytosine        within a DNA region in a sample from the individual where the        DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%,        98%, or 99% identical to, or comprises, a sequence selected from        the group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485;    -   b) comparing the methylation status of the at least one cytosine        to a threshold value for the biomarker, wherein the threshold        value distinguishes between individuals with and without thyroid        cancer, wherein the comparison of the methylation status to the        threshold value is predictive of the presence or absence of        thyroid cancer in the individual.

With regard to the embodiments, in some embodiments, the determiningstep comprises determining the methylation status of at least onecytosine in the DNA region corresponding to a nucleotide in a biomarker,wherein the biomarker is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%,97%, 98%, or 99% identical to, or comprises, a sequence selected fromthe group consisting of SEQ ID NO: 292, 293, 294, 295, 296, 297, 298,299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312,313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326,327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340,341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354,355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368,369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382,383, 384, 385, 386, 387, and 388.

In some embodiments, the determining step comprises determining themethylation status of the DNA region corresponding to a biomarker.

The sample can be from any body fluid. In some embodiments, the sampleis selected from blood serum, blood plasma, fine needle aspirate of thebreast, biopsy of the breast, ductal fluid, ductal lavage, feces, urine,sputum, saliva, semen, lavages, or tissue biopsy, such as biopsy of thelung, bronchial lavage or bronchial brushings in the case of lungcancer. In some embodiments, the sample is from a tumor or polyp. Insome embodiments, the sample is a biopsy from lung, kidney, liver,ovarian, head, neck, thyroid, bladder, cervical, colon, endometrial,esophageal, prostate or skin tissue. In some embodiments, the sample isfrom cell scrapes, washings, or resected tissues.

In some embodiments, the methylation status of at least one cytosine iscompared to the methylation status of a control locus. In someembodiments, the control locus is an endogenous control. In someembodiments, the control locus is an exogenous control.

In some embodiments, the determining step comprises determining themethylation status of at least one cytosine in at least two of the DNAregions.

In a further aspect, the invention provides computer implemented methodsfor determining the presence or absence of cancer (including but notlimited to cancers of the bladder, breast, cervix, colon, endometrium,esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, andthyroid, and melanoma) in an individual. In some embodiments, themethods comprise:

-   -   receiving, at a host computer, a methylation value representing        the methylation status of at least one cytosine within a DNA        region in a sample from the individual where the DNA region is        at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%        identical to, or comprises, a sequence is selected from the        group consisting of SEQ ID NO: 389, 390, 391, 392, 393, 394,        395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407,        408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,        421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,        434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446,        447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459,        460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,        473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and        485; and    -   comparing, in the host computer, the methylation value to a        threshold value, wherein the threshold value distinguishes        between individuals with and without cancer (including but not        limited to cancers of the bladder, breast, cervix, colon,        endometrium, esophagus, head and neck, liver, lung(s), ovaries,        prostate, rectum, and thyroid, and melanoma), wherein the        comparison of the methylation value to the threshold value is        predictive of the presence or absence of cancer (including but        not limited to cancers of the bladder, breast, cervix, colon,        endometrium, esophagus, head and neck, liver, lung(s), ovaries,        prostate, rectum, and thyroid, and melanoma) in the individual.

In some embodiments, the receiving step comprises receiving at least twomethylation values, the two methylation values representing themethylation status of at least one cytosine biomarkers from twodifferent DNA regions; and

-   -   the comparing step comprises comparing the methylation values to        one or more threshold value(s) wherein the threshold value        distinguishes between individuals with and without cancer        (including but not limited to cancers of the bladder, breast,        cervix, colon, endometrium, esophagus, head and neck, liver,        lung(s), ovaries, prostate, rectum, and thyroid, and melanoma),        wherein the comparison of the methylation value to the threshold        value is predictive of the presence or absence of cancer        (including but not limited to cancers of the bladder, breast,        cervix, colon, endometrium, esophagus, head and neck, liver,        lung(s), ovaries, prostate, rectum, and thyroid, and melanoma)        in the individual.

In another aspect, the invention provides computer program products fordetermining the presence or absence of cancer (including but not limitedto cancers of the bladder, breast, cervix, colon, endometrium,esophagus, head and neck, liver, lung(s), ovaries, prostate, rectum, andthyroid, and melanoma) in an individual. In some embodiments, thecomputer readable products comprise:

-   -   a computer readable medium encoded with program code, the        program code including:    -   program code for receiving a methylation value representing the        methylation status of at least one cytosine within a DNA region        in a sample from the individual where the DNA region is at least        90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99% identical        to, or comprises, a sequence selected from the group consisting        of SEQ ID NO: 389, 390, 391, 392, 393, 394, 395, 396, 397, 398,        399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411,        412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424,        425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437,        438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450,        451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463,        464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476,        477, 478, 479, 480, 481, 482, 483, 484, and 485; and    -   program code for comparing the methylation value to a threshold        value, wherein the threshold value distinguishes between        individuals with and without cancer (including but not limited        to cancers of the bladder, breast, cervix, colon, endometrium,        esophagus, head and neck, liver, lung(s), ovaries, prostate,        rectum, and thyroid, and melanoma), wherein the comparison of        the methylation value to the threshold value is predictive of        the presence or absence of cancer (including but not limited to        cancers of the bladder, breast, cervix, colon, endometrium,        esophagus, head and neck, liver, lung(s), ovaries, prostate,        rectum, and thyroid, and melanoma) in the individual.

In a further aspect, the invention provides kits for determining themethylation status of at least one biomarker. In some embodiments, thekits comprise:

-   -   a pair of polynucleotides capable of specifically amplifying at        least a portion of a DNA region where the DNA region is selected        from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393,        394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406,        407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419,        420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432,        433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445,        446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458,        459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471,        472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484,        and 485; and    -   a methylation-dependent or methylation sensitive restriction        enzyme and/or sodium bisulfite.

In some embodiments, the pair of polynucleotides are capable ofspecifically amplifying a biomarker selected from the group consistingof one or more of SEQ ID NOs: 292, 293, 294, 295, 296, 297, 298, 299,300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313,314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327,328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341,342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355,356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369,370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383,384, 385, 386, 387, and 388.

In some embodiments, the kits comprise at least two pairs ofpolynucleotides, wherein each pair is capable of specifically amplifyingat least a portion of a different DNA region.

In some embodiments, the kits further comprise a detectably labeledpolynucleotide probe that specifically detects the amplified biomarkerin a real time amplification reaction.

In a further aspect, the invention provides kits for determining themethylation status of at least one biomarker. In some embodiments, thekits comprise:

-   -   sodium bisulfite and polynucleotides to quantify the presence of        the converted methylated and or the converted unmethylated        sequence of at least one cytosine from a DNA region that is        selected from the group consisting of SEQ ID NOs: 389, 390, 391,        392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404,        405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417,        418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430,        431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443,        444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456,        457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469,        470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482,        483, 484, and 485.

In a further aspect, the invention provides kits for determining themethylation status of at least one biomarker. In some embodiments, thekits comprise:

-   -   sodium bisulfite, primers and adapters for whole genome        amplification, and polynucleotides to quantify the presence of        the converted methylated and or the converted unmethylated        sequence of at least one cytosine from a DNA region that is        selected from the group consisting of SEQ ID NOs: 389, 390, 391,        392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404,        405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417,        418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430,        431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443,        444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456,        457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469,        470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482,        483, 484, and 485.

In another aspect, the methods provide kits for determining themethylation status of at least one biomarker. In some embodiments, thekits comprise:

-   -   a methylation sensing restriction enzymes, primers and adapters        for whole genome amplification, and polynucleotides to quantify        the number of copies of at least a portion of a DNA region where        the DNA region is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%,        97%, 98%, or 99% identical to, or comprises, a sequence selected        from the group consisting of SEQ ID NO: 389, 390, 391, 392, 393,        394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406,        407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419,        420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432,        433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445,        446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458,        459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471,        472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484,        and 485.

In a further aspect, the invention provides kits for determining themethylation status of at least one biomarker. In some embodiments, thekits comprise:

-   -   a methylation sensing binding moiety and polynucleotides to        quantify the number of copies of at least a portion of a DNA        region where the DNA region is at least 90%, 91%, 92%, 93%, 94%,        95%, 96%, 97%, 98%, or 99% identical to, or comprises, a        sequence selected from the group consisting of SEQ ID NO: 389,        390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402,        403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,        416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428,        429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441,        442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454,        455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,        468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,        481, 482, 483, 484, and 485.

DEFINITIONS

“Methylation” refers to cytosine methylation at positions C5 or N4 ofcytosine, the N6 position of adenine or other types of nucleic acidmethylation. In vitro amplified DNA is unmethylated because in vitro DNAamplification methods do not retain the methylation pattern of theamplification template. However, “unmethylated DNA” or “methylated DNA”can also refer to amplified DNA whose original template was methylatedor methylated, respectively.

A “methylation profile” refers to a set of data representing themethylation states of one or more loci within a molecule of DNA frome.g., the genome of an individual or cells or tissues from anindividual. The profile can indicate the methylation state of every basein an individual, can comprise information regarding a subset of thebase pairs (e.g., the methylation state of specific restriction enzymerecognition sequence) in a genome, or can comprise information regardingregional methylation density of each locus.

“Methylation status” refers to the presence, absence and/or quantity ofmethylation at a particular nucleotide, or nucleotides within a portionof DNA. The methylation status of a particular DNA sequence (e.g., a DNAbiomarker or DNA region as described herein) can indicate themethylation state of every base in the sequence or can indicate themethylation state of a subset of the base pairs (e.g., of cytosines orthe methylation state of one or more specific restriction enzymerecognition sequences) within the sequence, or can indicate informationregarding regional methylation density within the sequence withoutproviding precise information of where in the sequence the methylationoccurs. The methylation status can optionally be represented orindicated by a “methylation value.” A methylation value can begenerated, for example, by quantifying the amount of intact DNA presentfollowing restriction digestion with a methylation dependent restrictionenzyme. In this example, if a particular sequence in the DNA isquantified using quantitative PCR, an amount of template DNAapproximately equal to a mock treated control indicates the sequence isnot highly methylated whereas an amount of template substantially lessthan occurs in the mock treated sample indicates the presence ofmethylated DNA at the sequence. Accordingly, a value, i.e., amethylation value, for example from the above described example,represents the methylation status and can thus be used as a quantitativeindicator of methylation status. This is of particular use when it isdesirable to compare the methylation status of a sequence in a sample toa threshold value.

A “methylation-dependent restriction enzyme” refers to a restrictionenzyme that cleaves or digests DNA at or in proximity to a methylatedrecognition sequence, but does not cleave DNA at or near the samesequence when the recognition sequence is not methylated.Methylation-dependent restriction enzymes include those that cut at amethylated recognition sequence (e.g., DpnI) and enzymes that cut at asequence near but not at the recognition sequence (e.g., McrBC). Forexample, McrBC's recognition sequence is 5′ RmC (N40-3000) RmC 3′ where“R” is a purine and “mC” is a methylated cytosine and “N40-3000”indicates the distance between the two RmC half sites for which arestriction event has been observed. McrBC generally cuts close to onehalf-site or the other, but cleavage positions are typically distributedover several base pairs, approximately 30 base pairs from the methylatedbase. McrBC sometimes cuts 3′ of both half sites, sometimes 5′ of bothhalf sites, and sometimes between the two sites. Exemplarymethylation-dependent restriction enzymes include, e.g., McrBC (see,e.g., U.S. Pat. No. 5,405,760), McrA, MrrA, BisI, GlaI and DpnI. One ofskill in the art will appreciate that any methylation-dependentrestriction enzyme, including homologs and orthologs of the restrictionenzymes described herein, is also suitable for use in the presentinvention.

A “methylation-sensitive restriction enzyme” refers to a restrictionenzyme that cleaves DNA at or in proximity to an unmethylatedrecognition sequence but does not cleave at or in proximity to the samesequence when the recognition sequence is methylated. Exemplarymethylation-sensitive restriction enzymes are described in, e.g.,McClelland et al., Nucleic Acids Res. 22(17):3640-59 (1994) andhttp://rebase.neb.com. Suitable methylation-sensitive restrictionenzymes that do not cleave DNA at or near their recognition sequencewhen a cytosine within the recognition sequence is methylated atposition C⁵ include, e.g., Aat II, Aci I, Acl I, Age I, Alu I, Asc I,Ase I, AsiS I, Bbe I, BsaA I, BsaH I, BsiE I, BsiW I, BsrF I, BssH II,BssK I, BstB I, BstN I, BstU I, Cla I, Eae L, Eag L, Fau I, Fse I, HhaI, HinP1 I, HinC II, Hpa II, Hpy99 I, HpyCH4 IV, Kas I, Mbo I, Mlu I,MapAl I, Msp I, Nae I, Nar I, Not I, Pml I, Pst I, Pvu I, Rsr II, SacII, Sap I, Sau3A I, Sfl I, Sfo I, SgrA I, Sma I, SnaB I, Tsc I, Xma I,and Zra I. Suitable methylation-sensitive restriction enzymes that donot cleave DNA at or near their recognition sequence when an adenosinewithin the recognition sequence is methylated at position N⁶ include,e.g., Mbo I. One of skill in the art will appreciate that anymethylation-sensitive restriction enzyme, including homologs andorthologs of the restriction enzymes described herein, is also suitablefor use in the present invention. One of skill in the art will furtherappreciate that a methylation-sensitive restriction enzyme that fails tocut in the presence of methylation of a cytosine at or near itsrecognition sequence may be insensitive to the presence of methylationof an adenosine at or near its recognition sequence. Likewise, amethylation-sensitive restriction enzyme that fails to cut in thepresence of methylation of an adenosine at or near its recognitionsequence may be insensitive to the presence of methylation of a cytosineat or near its recognition sequence. For example, Sau3AI is sensitive(i.e., fails to cut) to the presence of a methylated cytosine at or nearits recognition sequence, but is insensitive (i.e., cuts) to thepresence of a methylated adenosine at or near its recognition sequence.One of skill in the art will also appreciate that somemethylation-sensitive restriction enzymes are blocked by methylation ofbases on one or both strands of DNA encompassing of their recognitionsequence, while other methylation-sensitive restriction enzymes areblocked only by methylation on both strands, but can cut if arecognition site is hemi-methylated.

A “threshold value that distinguishes between individuals with andwithout” a particular disease refers to a value or range of values of aparticular measurement that can be used to distinguish between samplesfrom individuals with the disease and samples without the disease.Ideally, there is a threshold value or values that absolutelydistinguishes between the two groups (i.e., values from the diseasedgroup are always on one side (e.g., higher) of the threshold value andvalues from the healthy, non-diseased group are on the other side (e.g.,lower) of the threshold value). However, in many instances, thresholdvalues do not absolutely distinguish between diseased and non-diseasedsamples (for example, when there is some overlap of values generatedfrom diseased and non-diseased samples).

The phrase “corresponding to a nucleotide in a biomarker” refers to anucleotide in a DNA region that aligns with the same nucleotide (e.g. acytosine) in a biomarker sequence. Generally, as described herein,biomarker sequences are subsequences of (i.e., have 100% identity with)the DNA regions. Sequence comparisons can be performed using any BLASTincluding BLAST 2.2 algorithm with default parameters, described inAltschul et al., Nuc. Acids Res. 25:3389 3402 (1977) and Altschul etal., J. Mol. Biol. 215:403 410 (1990), respectively.

“Sensitivity” of a given biomarker refers to the percentage of tumorsamples that report a DNA methylation value above a threshold value thatdistinguishes between tumor and non-tumor samples. The percentage iscalculated as follows:

${Sensitivity} = {\left\lbrack \frac{\begin{pmatrix}{{{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {tumor}}\mspace{14mu}} \\{{samples}\mspace{14mu} {above}\mspace{14mu} {the}\mspace{14mu} {threshold}}\end{pmatrix}}{\begin{pmatrix}{{{the}\mspace{14mu} {total}\mspace{14mu} {number}\mspace{14mu} {of}}\mspace{14mu}} \\{{tumor}\mspace{14mu} {samples}\mspace{14mu} {tested}}\end{pmatrix}} \right\rbrack \times 100}$

The equation may also be stated as follows:

${Sensitivity} = {\left\lbrack \frac{\begin{pmatrix}{{{the}\mspace{14mu} {number}\mspace{14mu} {of}}\mspace{14mu}} \\{{true}\mspace{14mu} {positive}\mspace{14mu} {samples}}\end{pmatrix}}{\begin{matrix}{\left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {positive}\mspace{14mu} {samples}} \right) +} \\\left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {false}\mspace{14mu} {negative}\mspace{14mu} {samples}} \right)\end{matrix}} \right\rbrack \times 100}$

where true positive is defined as a histology-confirmed tumor samplethat reports a DNA methylation value above the threshold value (i.e. therange associated with disease), and false negative is defined as ahistology-confirmed tumor sample that reports a DNA methylation valuebelow the threshold value (i.e. the range associated with no disease).The value of sensitivity, therefore, reflects the probability that a DNAmethylation measurement for a given biomarker obtained from a knowndiseased sample will be in the range of disease-associated measurements.As defined here, the clinical relevance of the calculated sensitivityvalue represents an estimation of the probability that a given biomarkerwould detect the presence of a clinical condition when applied to apatient with that condition.

“Specificity” of a given biomarker refers to the percentage of non-tumorsamples that report a DNA methylation value below a threshold value thatdistinguishes between tumor and non-tumor samples. The percentage iscalculated as follows:

${Specificity} = {\left\lbrack \frac{\begin{pmatrix}{{{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {non}\text{-}{tumor}}\mspace{14mu}} \\{{samples}\mspace{14mu} {below}\mspace{14mu} {the}\mspace{14mu} {threshold}}\end{pmatrix}}{\begin{pmatrix}{{the}\mspace{14mu} {total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {non}\text{-}} \\{{tumor}\mspace{14mu} {samples}\mspace{14mu} {tested}}\end{pmatrix}} \right\rbrack \times 100}$

The equation may also be stated as follows:

${Specificity} = {\left\lbrack \frac{\begin{pmatrix}{{{the}\mspace{14mu} {number}\mspace{14mu} {of}}\mspace{14mu}} \\{{true}\mspace{14mu} {negative}\mspace{14mu} {samples}}\end{pmatrix}}{\left( {\left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {true}\mspace{14mu} {negative}\mspace{14mu} {samples}} \right) + \left( {{the}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {false}\mspace{14mu} {positive}\mspace{14mu} {samples}} \right)} \right)} \right\rbrack \times 100}$

where true negative is defined as a histology-confirmed non-tumor samplethat reports a DNA methylation value below the threshold value (i.e. therange associated with no disease), and false positive is defined as ahistology-confirmed non-tumor sample that reports DNA methylation valueabove the threshold value (i.e. the range associated with disease). Thevalue of specificity, therefore, reflects the probability that a DNAmethylation measurement for a given biomarker obtained from a knownnon-diseased sample will be in the range of non-disease associatedmeasurements. As defined here, the clinical relevance of the calculatedspecificity value represents an estimation of the probability that agiven biomarker would detect the absence of a clinical condition whenapplied to a patient without that condition.

Software for performing BLAST analyses is publicly available through theNational Center for Biotechnology Information. This algorithm involvesfirst identifying high scoring sequence pairs (HSPs) by identifyingshort words of length W in the query sequence, which either match orsatisfy some positive-valued threshold score T when aligned with a wordof the same length in a database sequence. T is referred to as theneighborhood word score threshold (Altschul et al., supra). Theseinitial neighborhood word hits act as seeds for initiating searches tofind longer HSPs containing them. The word hits are extended in bothdirections along each sequence for as far as the cumulative alignmentscore can be increased. Cumulative scores are calculated using, fornucleotide sequences, the parameters M (reward score for a pair ofmatching residues; always >0) and N (penalty score for mismatchingresidues; always <0). For amino acid sequences, a scoring matrix is usedto calculate the cumulative score. Extension of the word hits in eachdirection are halted when: the cumulative alignment score falls off bythe quantity X from its maximum achieved value; the cumulative scoregoes to zero or below, due to the accumulation of one or morenegative-scoring residue alignments; or the end of either sequence isreached. The BLAST algorithm parameters W, T, and X determine thesensitivity and speed of the alignment. The BLASTN program (fornucleotide sequences) uses as defaults a wordlength (W) of 11, anexpectation (E) of 10, M=5, N=−4 and a comparison of both strands. Foramino acid sequences, the BLASTP program uses as defaults a wordlengthof 3, and expectation (E) of 10, and the BLOSUM62 scoring matrix (seeHenikoff & Henikoff, Proc. Natl. Acad. Sci. USA 89:10915 (1989))alignments (B) of 50, expectation (E) of 10, M=5, N=−4, and a comparisonof both strands.

As used herein, the terms “nucleic acid,” “polynucleotide” and“oligonucleotide” refer to nucleic acid regions, nucleic acid segments,primers, probes, amplicons and oligomer fragments. The terms are notlimited by length and are generic to linear polymers ofpolydeoxyribonucleotides (containing 2-deoxy-D-ribose),polyribonucleotides (containing D-ribose), and any other N-glycoside ofa purine or pyrimidine base, or modified purine or pyrimidine bases.These terms include double- and single-stranded DNA, as well as double-and single-stranded RNA.

A nucleic acid, polynucleotide or oligonucleotide can comprise, forexample, phosphodiester linkages or modified linkages including, but notlimited to phosphotriester, phosphoramidate, siloxane, carbonate,carboxymethylester, acetamidate, carbamate, thioether, bridgedphosphoramidate, bridged methylene phosphonate, phosphorothioate,methylphosphonate, phosphorodithioate, bridged phosphorothioate orsulfone linkages, and combinations of such linkages.

A nucleic acid, polynucleotide or oligonucleotide can comprise the fivebiologically occurring bases (adenine, guanine, thymine, cytosine anduracil) and/or bases other than the five biologically occurring bases.For example, a polynucleotide of the invention can contain one or moremodified, non-standard, or derivatized base moieties, including, but notlimited to, N⁶-methyl-adenine, N⁶-tert-butyl-benzyl-adenine, imidazole,substituted imidazoles, 5-fluorouracil, 5-bromouracil, 5-chlorouracil,5-iodouracil, hypoxanthine, xanthine, 4-acetylcytosine,5-(carboxyhydroxymethyl)uracil,5-carboxymethylaminomethyl-2-thiouridine,5-carboxymethylaminomethyluracil, dihydrouracil,beta-D-galactosylqueosine, inosine, N⁶-isopentenyladenine,1-methylguanine, 1-methylinosine, 2,2-dimethylguanine, 2-methyladenine,2-methylguanine, 3-methylcytosine, 5-methylcytosine, N⁶-methyladenine,7-methylguanine, 5-methylaminomethyluracil,5-methoxyaminomethyl-2-thiouracil, beta-D mannosylqueosine,5′-methoxycarboxymethyluracil, 5-methoxyuracil,2-methylthio-N6-isopentenyladenine, uracil-5-oxyacetic acid (v),wybutoxosine, pseudouracil, queosine, 2-thiocytosine,5-methyl-2-thiouracil, 2-thiouracil, 4-thiouracil, uracil-5-oxyaceticacidmethylester, 3-(3-amino-3-N-2-carboxypropyl) uracil, (acp3)w,2,6-diaminopurine, and 5-propynyl pyrimidine. Other examples ofmodified, non-standard, or derivatized base moieties may be found inU.S. Pat. Nos. 6,001,611; 5,955,589; 5,844,106; 5,789,562; 5,750,343;5,728,525; and 5,679,785.

Furthermore, a nucleic acid, polynucleotide or oligonucleotide cancomprise one or more modified sugar moieties including, but not limitedto, arabinose, 2-fluoroarabinose, xylulose, and a hexose.

DETAILED DESCRIPTION OF THE INVENTION I. Introduction

The present invention is based, in part, on the discovery that sequencesin certain DNA regions are methylated in cancer cells, but not normalcells. Specifically, the inventors have found that methylation ofbiomarkers within the DNA regions described herein are associated withvarious types of cancer.

In view of this discovery, the inventors have recognized that methodsfor detecting the biomarker sequences and DNA regions comprising thebiomarker sequences as well as sequences adjacent to the biomarkers thatcontain a significant amount of CpG subsequences, methylation of the DNAregions, and/or expression of the genes regulated by the DNA regions canbe used to detect cancer cells. Detecting cancer cells allows fordiagnostic tests that detect disease, assess the risk of contractingdisease, determining a predisposition to disease, stage disease,diagnose disease, monitor disease, and/or aid in the selection oftreatment for a person with disease.

II. Methylation Biomarkers

In some embodiments, the presence or absence or quantity of methylationof the chromosomal DNA within a DNA region or portion thereof (e.g., atleast one cytosine) selected from SEQ ID Nos: 389-485 is detected.Portions of the DNA regions described herein will comprise at least onepotential methylation site (i.e., a cytosine) and can in someembodiments generally comprise 2, 3, 4, 5, 10, or more potentialmethylation sites. In some embodiments, the methylation status of allcytosines within at least 20, 50, 100, 200, 500 or more contiguous basepairs of the DNA region are determined.

Some of the DNA regions overlap with each other, indicating thatmethylation can be detected in a larger chromosomal region as defined bythe boundaries of the overlapping sequences. For example, SEQ ID NO:402overlaps with SEQ ID NO:403; SEQ ID NOs: 407, 408 and 409 overlap witheach other; SEQ ID NO:425 overlaps with SEQ ID NO:426; SEQ ID NOs:411,427, 428, 442, 443, 444 overlap with each other; and SEQ ID NO:429overlaps with SEQ ID NO:445. Thus, for example, methylation can bedetected for the purposes described herein to detect the methylationstatus of at least one cytosine in a sequence from:

either SEQ ID NO: 402 or 403;

any of SEQ ID NO:403; SEQ ID NOs: 407, 408 or 409;

either SEQ ID NO:425 or SEQ ID NO:426;

any of SEQ ID NOs:411, 427, 428, 442, 443, 444;

either SEQ ID NO:429 or SEQ ID NO:445.

In some embodiments, the methylation of more than one DNA region (orportion thereof) is detected. In some embodiments, the methylationstatus of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,91, 92, 93, 94, 95, 96, or 97 of the DNA regions is determined.

In some embodiments of the invention, the methylation of a DNA region orportion thereof is determined and then normalized (e.g., compared) tothe methylation of a control locus. Typically the control locus willhave a known, relatively constant, methylation status. For example, thecontrol sequence can be previously determined to have no, some or a highamount of methylation, thereby providing a relative constant value tocontrol for error in detection methods, etc., unrelated to the presenceor absence of cancer. In some embodiments, the control locus isendogenous, i.e., is part of the genome of the individual sampled. Forexample, in mammalian cells, the testes-specific histone 2B gene (hTH2Bin human) gene is known to be methylated in all somatic tissues excepttestes. Alternatively, the control locus can be an exogenous locus,i.e., a DNA sequence spiked into the sample in a known quantity andhaving a known methylation status.

A DNA region comprises a nucleic acid including one or more methylationsites of interest (e.g., a cytosine, a “microarray feature,” or anamplicon amplified from select primers) and flanking nucleic acidsequences (i.e., “wingspan”) of up to 4 kilobases (kb) in either or bothof the 3′ or 5′ direction from the amplicon. This range corresponds tothe lengths of DNA fragments obtained by randomly fragmenting the DNAbefore screening for differential methylation between DNA in two or moresamples (e.g. carrying out methods used to initially identifydifferentially methylated sequences as described in the Examples,below). In some embodiments, the wingspan of the one or more DNA regionsis about 0.5 kb, 0.75 kb, 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kbor 4.0 kb in both 3′ and 5′ directions relative to the sequencerepresented by the microarray feature.

The methylation sites in a DNA region can reside in non-codingtranscriptional control sequences (e.g. promoters, enhancers, etc.) orin coding sequences, including introns and exons of the designated geneslisted in Tables 1 and 2 and in section “SEQUENCE LISTING.” In someembodiments, the methods comprise detecting the methylation status inthe promoter regions (e.g., comprising the nucleic acid sequence that isabout 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or 4.0 kb 5′ fromthe transcriptional start site through to the transcriptional startsite) of one or more of the genes identified in Tables 1 and 2 and insection “SEQUENCE LISTING.”

The DNA regions of the invention also include naturally occurringvariants, including for example, variants occurring in different subjectpopulations and variants arising from single nucleotide polymorphisms(SNPs). SNPs encompasses insertions and deletions of varying size andsimple sequence repeats, such as dinucleotides and trinucleotiderepeats. Variants include nucleic acid sequences from the same DNAregion (e.g. as set forth in Tables 1 and 2 and in section “SEQUENCELISTING”) sharing at least 90%, 95%, 98%, 99% sequence identity, i.e.,having one or more deletions, additions, substitutions, invertedsequences, etc., relative to the DNA regions described herein.

III. Methods for Determining Methylation

Any method for detecting DNA methylation can be used in the methods ofthe present invention.

In some embodiments, methods for detecting methylation include randomlyshearing or randomly fragmenting the genomic DNA, cutting the DNA with amethylation-dependent or methylation-sensitive restriction enzyme andsubsequently selectively identifying and/or analyzing the cut or uncutDNA. Selective identification can include, for example, separating cutand uncut DNA (e.g., by size) and quantifying a sequence of interestthat was cut or, alternatively, that was not cut. See, e.g., U.S. Pat.No. 7,186,512. Alternatively, the method can encompass amplifying intactDNA after restriction enzyme digestion, thereby only amplifying DNA thatwas not cleaved by the restriction enzyme in the area amplified. See,e.g., U.S. patent application Ser. Nos. 10/971,986; 11/071,013; and10/971,339. In some embodiments, amplification can be performed usingprimers that are gene specific. Alternatively, adaptors can be added tothe ends of the randomly fragmented DNA, the DNA can be digested with amethylation-dependent or methylation-sensitive restriction enzyme,intact DNA can be amplified using primers that hybridize to the adaptorsequences. In this case, a second step can be performed to determine thepresence, absence or quantity of a particular gene in an amplified poolof DNA. In some embodiments, the DNA is amplified using real-time,quantitative PCR.

In some embodiments, the methods comprise quantifying the averagemethylation density in a target sequence within a population of genomicDNA. In some embodiments, the method comprises contacting genomic DNAwith a methylation-dependent restriction enzyme or methylation-sensitiverestriction enzyme under conditions that allow for at least some copiesof potential restriction enzyme cleavage sites in the locus to remainuncleaved; quantifying intact copies of the locus; and comparing thequantity of amplified product to a control value representing thequantity of methylation of control DNA, thereby quantifying the averagemethylation density in the locus compared to the methylation density ofthe control DNA.

The quantity of methylation of a locus of DNA can be determined byproviding a sample of genomic DNA comprising the locus, cleaving the DNAwith a restriction enzyme that is either methylation-sensitive ormethylation-dependent, and then quantifying the amount of intact DNA orquantifying the amount of cut DNA at the DNA locus of interest. Theamount of intact or cut DNA will depend on the initial amount of genomicDNA containing the locus, the amount of methylation in the locus, andthe number (i.e., the fraction) of nucleotides in the locus that aremethylated in the genomic DNA. The amount of methylation in a DNA locuscan be determined by comparing the quantity of intact DNA or cut DNA toa control value representing the quantity of intact DNA or cut DNA in asimilarly-treated DNA sample. The control value can represent a known orpredicted number of methylated nucleotides. Alternatively, the controlvalue can represent the quantity of intact or cut DNA from the samelocus in another (e.g., normal, non-diseased) cell or a second locus.

By using at least one methylation-sensitive or methylation-dependentrestriction enzyme under conditions that allow for at least some copiesof potential restriction enzyme cleavage sites in the locus to remainuncleaved and subsequently quantifying the remaining intact copies andcomparing the quantity to a control, average methylation density of alocus can be determined. If the methylation-sensitive restriction enzymeis contacted to copies of a DNA locus under conditions that allow for atleast some copies of potential restriction enzyme cleavage sites in thelocus to remain uncleaved, then the remaining intact DNA will bedirectly proportional to the methylation density, and thus may becompared to a control to determine the relative methylation density ofthe locus in the sample. Similarly, if a methylation-dependentrestriction enzyme is contacted to copies of a DNA locus underconditions that allow for at least some copies of potential restrictionenzyme cleavage sites in the locus to remain uncleaved, then theremaining intact DNA will be inversely proportional to the methylationdensity, and thus may be compared to a control to determine the relativemethylation density of the locus in the sample. Such assays aredisclosed in, e.g., U.S. patent application Ser. No. 10/971,986.

Kits for the above methods can include, e.g., one or more ofmethylation-dependent restriction enzymes, methylation-sensitiverestriction enzymes, amplification (e.g., PCR) reagents, probes and/orprimers.

Quantitative amplification methods (e.g., quantitative PCR orquantitative linear amplification) can be used to quantify the amount ofintact DNA within a locus flanked by amplification primers followingrestriction digestion. Methods of quantitative amplification aredisclosed in, e.g., U.S. Pat. Nos. 6,180,349; 6,033,854; and 5,972,602,as well as in, e.g., Gibson et al., Genome Research 6:995-1001 (1996);DeGraves, et al., Biotechniques 34(1):106-10, 112-5 (2003); Deiman B, etal., Mol Biotechnol. 20(2):163-79 (2002). Amplifications may bemonitored in “real time.”

Additional methods for detecting DNA methylation can involve genomicsequencing before and after treatment of the DNA with bisulfite. See,e.g., Frommer et al., Proc. Natl. Acad. Sci. USA 89:1827-1831 (1992).When sodium bisulfite is contacted to DNA, unmethylated cytosine isconverted to uracil, while methylated cytosine is not modified.

In some embodiments, restriction enzyme digestion of PCR productsamplified from bisulfite-converted DNA is used to detect DNAmethylation. See, e.g., Sadri & Hornsby, Nucl. Acids Res. 24:5058-5059(1996); Xiong & Laird, Nucleic Acids Res. 25:2532-2534 (1997).

In some embodiments, a MethyLight assay is used alone or in combinationwith other methods to detect DNA methylation (see, Eads et al., CancerRes. 59:2302-2306 (1999)). Briefly, in the MethyLight process genomicDNA is converted in a sodium bisulfite reaction (the bisulfite processconverts unmethylated cytosine residues to uracil). Amplification of aDNA sequence of interest is then performed using PCR primers thathybridize to CpG dinucleotides. By using primers that hybridize only tosequences resulting from bisulfite conversion of unmethylated DNA, (oralternatively to methylated sequences that are not converted)amplification can indicate methylation status of sequences where theprimers hybridize. Similarly, the amplification product can be detectedwith a probe that specifically binds to a sequence resulting frombisulfite treatment of a unmethylated (or methylated) DNA. If desired,both primers and probes can be used to detect methylation status. Thus,kits for use with MethyLight can include sodium bisulfite as well asprimers or detectably-labeled probes (including but not limited toTaqman or molecular beacon probes) that distinguish between methylatedand unmethylated DNA that have been treated with bisulfite. Other kitcomponents can include, e.g., reagents necessary for amplification ofDNA including but not limited to, PCR buffers, deoxynucleotides; and athermostable polymerase.

In some embodiments, a Ms-SNuPE (Methylation-sensitive Single NucleotidePrimer Extension) reaction is used alone or in combination with othermethods to detect DNA methylation (see, Gonzalgo & Jones, Nucleic AcidsRes. 25:2529-2531 (1997)). The Ms-SNuPE technique is a quantitativemethod for assessing methylation differences at specific CpG sites basedon bisulfite treatment of DNA, followed by single-nucleotide primerextension (Gonzalgo & Jones, supra). Briefly, genomic DNA is reactedwith sodium bisulfite to convert unmethylated cytosine to uracil whileleaving 5-methylcytosine unchanged. Amplification of the desired targetsequence is then performed using PCR primers specific forbisulfite-converted DNA, and the resulting product is isolated and usedas a template for methylation analysis at the CpG site(s) of interest.

Typical reagents (e.g., as might be found in a typical Ms-SNuPE-basedkit) for Ms-SNuPE analysis can include, but are not limited to: PCRprimers for specific gene (or methylation-altered DNA sequence or CpGisland); optimized PCR buffers and deoxynucleotides; gel extraction kit;positive control primers; Ms-SNuPE primers for a specific gene; reactionbuffer (for the Ms-SNuPE reaction); and detectably-labeled nucleotides.Additionally, bisulfite conversion reagents may include: DNAdenaturation buffer; sulfonation buffer; DNA recovery regents or kit(e.g., precipitation, ultrafiltration, affinity column); desulfonationbuffer; and DNA recovery components.

In some embodiments, a methylation-specific PCR (“MSP”) reaction is usedalone or in combination with other methods to detect DNA methylation. AnMSP assay entails initial modification of DNA by sodium bisulfite,converting all unmethylated, but not methylated, cytosines to uracil,and subsequent amplification with primers specific for methylated versusunmethylated DNA. See, Herman et al., Proc. Natl. Acad. Sci. USA93:9821-9826, (1996); U.S. Pat. No. 5,786,146.

Additional methylation detection methods include, but are not limitedto, methylated CpG island amplification (see, Toyota et al., Cancer Res.59:2307-12 (1999)) and those described in, e.g., U.S. Patent Publication2005/0069879; Rein, et al. Nucleic Acids Res. 26 (10): 2255-64 (1998);Olek, et al. Nat. Genet. 17(3): 275-6 (1997); and PCT Publication No. WO00/70090.

It is well known that methylation of genomic DNA can affect expression(transcription and/or translation) of nearby gene sequences. Therefore,in some embodiments, the methods include the step of correlating themethylation status of at least one cytosine in a DNA region with theexpression of nearby coding sequences, as described in Tables 1 and 2and in section “SEQUENCE LISTING.” For example, expression of genesequences within about 1.0 kb, 1.5 kb, 2.0 kb, 2.5 kb, 3.0 kb, 3.5 kb or4.0 kb in either the 3′ or 5′ direction from the cytosine of interest inthe DNA region can be detected. Methods for measuring transcriptionand/or translation of a particular gene sequence are well known in theart. See, for example, Ausubel, Current Protocols in Molecular Biology,1987-2006, John Wiley & Sons; and Sambrook and Russell, MolecularCloning: A Laboratory Manual, 3rd Edition, 2000, Cold Spring HarborLaboratory Press. In some embodiments, the gene or protein expression ofa gene in Tables 1 and 2 and in section “SEQUENCE LISTING” is comparedto a control, for example, the methylation status in the DNA regionand/or the expression of a nearby gene sequence from a sample from anindividual known to be negative for cancer or known to be positive forcancer, or to an expression level that distinguishes between cancer andnoncancer states. Such methods, like the methods of detectingmethylation described herein, are useful in providing diagnosis,prognosis, etc., of cancer.

In some embodiments, the methods further comprise the step ofcorrelating the methylation status and expression of one or more of thegene regions identified in Tables 1 and 2 and in section “SEQUENCELISTING.”

IV. Cancer Detection

The present biomarkers and methods can be used in the diagnosis,prognosis, classification, prediction of disease risk, detection ofrecurrence of disease, and selection of treatment of a number of typesof cancers. A cancer at any stage of progression can be detected, suchas primary, metastatic, and recurrent cancers. Information regardingnumerous types of cancer can be found, e.g., from the American CancerSociety (available on the worldwide web at cancer.org), or from, e.g.,Harrison's Principles of Internal Medicine, Kaspar, et al., eds., 16thEdition, 2005, McGraw-Hill, Inc. Exemplary cancers that can be detectedinclude lung, breast, renal, liver, ovarian, head and neck, thyroid,bladder, cervical, colon, endometrial, esophageal, prostate cancer ormelanoma.

The present invention provides methods for determining whether or not amammal (e.g., a human) has cancer, whether or not a biological sampletaken from a mammal contains cancerous cells, estimating the risk orlikelihood of a mammal developing cancer, classifying cancer types andstages, monitoring the efficacy of anti-cancer treatment, or selectingthe appropriate anti-cancer treatment in a mammal with cancer. Suchmethods are based on the discovery that cancer cells have a differentmethylation status than normal cells in the DNA regions described in theinvention. Accordingly, by determining whether or not a cell containsdifferentially methylated sequences in the DNA regions as describedherein, it is possible to determine whether or not the cell iscancerous.

In numerous embodiments of the present invention, the presence ofmethylated nucleotides in the diagnostic biomarker sequences of theinvention is detected in a biological sample, thereby detecting thepresence or absence of cancerous cells in the biological sample.

In some embodiments, the biological sample comprises a tissue samplefrom a tissue suspected of containing cancerous cells. For example, inan individual suspected of having cancer, breast tissue, lymph tissue,lung tissue, brain tissue, or blood can be evaluated. Alternatively,lung, renal, liver, ovarian, head and neck, thyroid, bladder, cervical,colon, endometrial, esophageal, prostate, or skin tissue can beevaluated. The tissue or cells can be obtained by any method known inthe art including, e.g., by surgery, biopsy, phlebotomy, swab, nippledischarge, stool, etc. In other embodiments, a tissue sample known tocontain cancerous cells, e.g., from a tumor, will be analyzed for thepresence or quantity of methylation at one or more of the diagnosticbiomarkers of the invention to determine information about the cancer,e.g., the efficacy of certain treatments, the survival expectancy of theindividual, etc. In some embodiments, the methods will be used inconjunction with additional diagnostic methods, e.g., detection of othercancer biomarkers, etc.

Genomic DNA samples can be obtained by any means known in the art. Incases where a particular phenotype or disease is to be detected, DNAsamples should be prepared from a tissue of interest, or as appropriate,from blood. For example, DNA can be prepared from biopsy tissue todetect the methylation state of a particular locus associated withcancer. The nucleic acid-containing specimen used for detection ofmethylated loci (see, e.g. Ausubel et al., Current Protocols inMolecular Biology (1995 supplement)) may be from any source and may beextracted by a variety of techniques such as those described by Ausubelet al., Current Protocols in Molecular Biology (1995) or Sambrook etal., Molecular Cloning, A Laboratory Manual (3rd ed. 2001).

The methods of the invention can be used to evaluate individuals knownor suspected to have cancer or as a routine clinical test, i.e., in anindividual not necessarily suspected to have cancer. Further diagnosticassays can be performed to confirm the status of cancer in theindividual.

Further, the present methods may be used to assess the efficacy of acourse of treatment. For example, the efficacy of an anti-cancertreatment can be assessed by monitoring DNA methylation of the biomarkersequences described herein over time in a mammal having cancer. Forexample, a reduction or absence of methylation in any of the diagnosticbiomarkers of the invention in a biological sample taken from a mammalfollowing a treatment, compared to a level in a sample taken from themammal before, or earlier in, the treatment, indicates efficacioustreatment.

The methods detecting cancer can comprise the detection of one or moreother cancer-associated polynucleotide or polypeptides sequences.Accordingly, detection of methylation of any one or more of thediagnostic biomarkers of the invention can be used either alone, or incombination with other biomarkers, for the diagnosis or prognosis ofcancer.

The methods of the present invention can be used to determine theoptimal course of treatment in a mammal with cancer. For example, thepresence of methylated DNA within any of the diagnostic biomarkers ofthe invention or an increased quantity of methylation within any of thediagnostic biomarkers of the invention can indicate a reduced survivalexpectancy of a mammal with cancer, thereby indicating a more aggressivetreatment for the mammal. In addition, a correlation can be readilyestablished between the presence, absence or quantity of methylation ata diagnostic biomarker, as described herein, and the relative efficacyof one or another anti-cancer agent. Such analyses can be performed,e.g., retrospectively, i.e., by detecting methylation in one or more ofthe diagnostic genes in samples taken previously from mammals that havesubsequently undergone one or more types of anti-cancer therapy, andcorrelating the known efficacy of the treatment with the presence,absence or levels of methylation of one or more of the diagnosticbiomarkers.

In making a diagnosis, prognosis, risk assessment, classification,detection of recurrence or selection of therapy based on the presence orabsence of methylation in at least one of the diagnostic biomarkers, thequantity of methylation may be compared to a threshold value thatdistinguishes between one diagnosis, prognosis, risk assessment,classification, etc., and another. For example, a threshold value canrepresent the degree of methylation found at a particular DNA regionthat adequately distinguishes between cancer samples and normal sampleswith a desired level of sensitivity and specificity. It is understoodthat a threshold value will likely vary depending on the assays used tomeasure methylation, but it is also understood that it is a relativelysimple matter to determine a threshold value or range by measuringmethylation of a DNA sequence in cancer samples and normal samples usingthe particular desired assay and then determining a value thatdistinguishes at least a majority of the cancer samples from a majorityof non-cancer samples. If methylation of two or more DNA regions isdetected, two or more different threshold values (one for each DNAregion) will often, but not always, be used. Comparisons between aquantity of methylation of a sequence in a sample and a threshold valuecan be performed in any way known in the art. For example, a manualcomparison can be made or a computer can compare and analyze the valuesto detect disease, assess the risk of contracting disease, determining apredisposition to disease, stage disease, diagnose disease, monitor, oraid in the selection of treatment for a person with disease.

In some embodiments, threshold values provide at least a specifiedsensitivity and specificity for detection of a particular cancer type.In some embodiments, the threshold value allows for at least a 50%, 60%,70%, or 80% sensitivity and specificity for detection of a specificcancer, e.g., breast, lung, renal, liver, ovarian, head and neck,thyroid, bladder, cervical, colon, endometrial, esophageal, prostatecancer or melanoma.

In embodiments involving prognosis of cancer (including, for example,the prediction of progression of non-malignant lesions to invasivecarcinoma, prediction of metastasis, prediction of disease recurrance orprediction of a response to a particular treatment), in someembodiments, the threshold value is set such that there is at least 10,20, 30, 40, 50, 60, 70, 80% or more sensitivity and at least 70%specificity with regard to detecting cancer.

In some embodiments, the methods comprise recording a diagnosis,prognosis, risk assessment or classification, based on the methylationstatus determined from an individual. Any type of recordation iscontemplated, including electronic recordation, e.g., by a computer.

V. Kits

This invention also provides kits for the detection and/orquantification of the diagnostic biomarkers of the invention, orexpression or methylation thereof using the methods described herein.

For kits for detection of methylation, the kits of the invention cancomprise at least one polynucleotide that hybridizes to at least one ofthe diagnostic biomarker sequences of the invention and at least onereagent for detection of gene methylation. Reagents for detection ofmethylation include, e.g., sodium bisulfite, polynucleotides designed tohybridize to sequence that is the product of a biomarker sequence of theinvention if the biomarker sequence is not methylated (e.g., containingat least one C→U conversion), and/or a methylation-sensitive ormethylation-dependent restriction enzyme. The kits can provide solidsupports in the form of an assay apparatus that is adapted to use in theassay. The kits may further comprise detectable labels, optionallylinked to a polynucleotide, e.g., a probe, in the kit. Other materialsuseful in the performance of the assays can also be included in thekits, including test tubes, transfer pipettes, and the like. The kitscan also include written instructions for the use of one or more ofthese reagents in any of the assays described herein.

In some embodiments, the kits of the invention comprise one or more(e.g., 1, 2, 3, 4, or more) different polynucleotides (e.g., primersand/or probes) capable of specifically amplifying at least a portion ofa DNA region where the DNA region is a sequence selected from the groupconsisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396, 397,398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411,412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425,426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439,440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453,454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481,482, 483, 484, and 485. Optionally, one or more detectably-labeledpolypeptide capable of hybridizing to the amplified portion can also beincluded in the kit. In some embodiments, the kits comprise sufficientprimers to amplify 2, 3, 4, 5, 6, 7, 8, 9, 10, or more different DNAregions or portions thereof, and optionally include detectably-labeledpolynucleotides capable of hybridizing to each amplified DNA region orportion thereof. The kits further can comprise a methylation-dependentor methylation sensitive restriction enzyme and/or sodium bisulfite.

In some embodiments, the kits comprise sodium bisulfite, primers andadapters (e.g., oligonucleotides that can be ligated or otherwise linkedto genomic fragments) for whole genome amplification, andpolynucleotides (e.g., detectably-labeled polynucleotoides) to quantifythe presence of the converted methylated and or the convertedunmethylated sequence of at least one cytosine from a DNA region that isselected from the group consisting of SEQ ID NOs: 389, 390, 391, 392,393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406,407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434,435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448,449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462,463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476,477, 478, 479, 480, 481, 482, 483, 484, and 485.

In some embodiments, the kits comprise a methylation sensing restrictionenzymes (e.g., a methylation-dependent restriction enzyme and/or amethylation-sensitive restriction enzyme), primers and adapters forwhole genome amplification, and polynucleotides to quantify the numberof copies of at least a portion of a DNA region where the DNA region isselected from the group consisting of SEQ ID NOs: 389, 390, 391, 392,393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406,407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420,421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434,435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448,449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462,463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476,477, 478, 479, 480, 481, 482, 483, 484, and 485.

In some embodiments, the kits comprise a methylation binding moiety andone or more polynucleotides to quantify the number of copies of at leasta portion of a DNA region where the DNA region is selected from thegroup consisting of SEQ ID NOs: 389, 390, 391, 392, 393, 394, 395, 396,397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410,411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424,425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438,439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452,453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466,467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,481, 482, 483, 484, and 485. A methylation binding moiety refers to amolecule (e.g., a polypeptide) that specifically binds tomethyl-cytosine. Examples include restriction enzymes or fragmentsthereof that lack DNA cutting activity but retain the ability to bindmethylated DNA, antibodies that specifically bind to methylated DNA,etc.).

VI. Computer-Based Methods

The calculations for the methods described herein can involvecomputer-based calculations and tools. For example, a methylation valuefor a DNA region or portion thereof can be compared by a computer to athreshold value, as described herein. The tools are advantageouslyprovided in the form of computer programs that are executable by ageneral purpose computer system (referred to herein as a “hostcomputer”) of conventional design. The host computer may be configuredwith many different hardware components and can be made in manydimensions and styles (e.g., desktop PC, laptop, tablet PC, handheldcomputer, server, workstation, mainframe). Standard components, such asmonitors, keyboards, disk drives, CD and/or DVD drives, and the like,may be included. Where the host computer is attached to a network, theconnections may be provided via any suitable transport media (e.g.,wired, optical, and/or wireless media) and any suitable communicationprotocol (e.g., TCP/IP); the host computer may include suitablenetworking hardware (e.g., modem, Ethernet card, WiFi card). The hostcomputer may implement any of a variety of operating systems, includingUNIX, Linux, Microsoft Windows, MacOS, or any other operating system.

Computer code for implementing aspects of the present invention may bewritten in a variety of languages, including PERL, C, C++, Java,JavaScript, VBScript, AWK, or any other scripting or programminglanguage that can be executed on the host computer or that can becompiled to execute on the host computer. Code may also be written ordistributed in low level languages such as assembler languages ormachine languages.

The host computer system advantageously provides an interface via whichthe user controls operation of the tools. In the examples describedherein, software tools are implemented as scripts (e.g., using PERL),execution of which can be initiated by a user from a standard commandline interface of an operating system such as Linux or UNIX. Thoseskilled in the art will appreciate that commands can be adapted to theoperating system as appropriate. In other embodiments, a graphical userinterface may be provided, allowing the user to control operations usinga pointing device. Thus, the present invention is not limited to anyparticular user interface.

Scripts or programs incorporating various features of the presentinvention may be encoded on various computer readable media for storageand/or transmission. Examples of suitable media include magnetic disk ortape, optical storage media such as compact disk (CD) or DVD (digitalversatile disk), flash memory, and carrier signals adapted fortransmission via wired, optical, and/or wireless networks conforming toa variety of protocols, including the Internet.

EXAMPLES Example 1 Identification of Cancer DNA Methylation Biomarkers

Loci that are differentially methylated in tumors relative to matchedadjacent histologically normal tissue were identified using a DNAmicroarray-based technology platform that utilizes themethylation-dependent restriction enzyme McrBC. See, e.g. U.S. Pat. No.7,186,512. In the discovery phase, cancer tissue and normal tissuesamples were analyzed. Purified genomic DNA from each sample (60 μg) wasrandomly sheared to a range of 1 to 4 kb. The sheared DNA of each samplewas then split into four equal portions of 15 μg each. Two portions weredigested with McrBC under the following conditions: 15 μg shearedgenomic DNA, 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serumalbumin (New England Biolabs), 2 mM GTP (Roche) and 120 units of McrBCenzyme (New England Biolabs) in a total volume of 600 μL at 37° C. forapproximately 12 hours. These two portions represent a technicalreplicate of McrBC digestion (Treated 1 and Treated 2). The remainingtwo 15 μg portions were mock treated under identical conditions with theexception that 12 μL of sterile 50% glycerol were added instead of McrBCenzyme. These two portions represent a technical replicate of mocktreatment (Untreated 1 and Untreated 2). All reactions were treated with5 μL proteinase K (50 mg/mL) for 1 hour at 50° C., and precipitated withEtOH under standard conditions. Pellets were washed twice with 70% EtOH,dried and resuspended in 30 μL H2O. Samples were then resolved on a 1%low melting point SeaPlaque GTG Agarose gel (Cambridge Bio Sciences).Untreated 1 and Treated 1 portions were resolved side-by-side, as wereUntreated 2 and Treated 2 portions. 1 kb DNA sizing ladder was resolvedadjacent to each untreated/treated pair to guide accurate gel sliceexcision. Gels were visualized with long-wave UV, and gel slicesincluding DNA within the modal size range of the untreated fraction(approximately 1-4 kb) were excised with a clean razor blade. DNA wasextracted from gel slices using gel extraction kits (Qiagen).

McrBC recognizes a pair of methylated cytosine residues in the context5′-Pu^(m)C (N₄₀₋₂₀₀₀) Pu^(m)C-3′ (where Pu=A or G,^(m)C=5-methylcytosine, and N=any nucleotide), and cleaves withinapproximately 30 base-pairs from one of the methylated cytosineresidues. Therefore, loci that include high local densities of Pu ^(m)Cwill be cleaved to a greater extent than loci that include low localdensities of Pu ^(m)C. Since Untreated and Treated portions wereresolved by agarose gel electrophoresis, and DNA within the modal sizerange of the Untreated portions were excised and gel extracted, theUntreated portions represent the entire fragmented genome of the samplewhile the Treated portions are depleted of DNA fragments including Pu^(m)C. Fractions were analyzed using a duplicated dye swap microarrayhybridization paradigm. For example, equal mass (200 ng) of Untreated 1and Treated 1 fraction DNA were used as template for labeling with Cy3and Cy5, respectively, and hybridized to a microarray (described below).Equal mass (200 ng) of the same Untreated 1 and Treated 1 fraction DNAwere used as template for labeling with Cy5 and Cy3, respectively, andhybridized to a second microarray (these two hybridizations represent adye swap of Untreated 1/Treated 1 fractions). Equal mass (200 ng) ofUntreated 2 and Treated 2 fraction DNA were used as template forlabeling with Cy3 and Cy5, respectively, and hybridized to a thirdmicroarray. Finally, equal mass (200 ng) of Untreated 2 and Treated 2fraction DNA were used as template for labeling with Cy5 and Cy3,respectively, and hybridized to a fourth microarray (the final twohybridizations represent a technical replicate of the first dye swap).All DNA samples (e.g., tumor samples and adjacent normal samples) wereanalyzed in this way.

The microarray described in this Example consists of 380,727 features.Each 50mer oligonucleotide feature is represented by three replicatesper microarray slide, yielding a total of 124,877 unique feature probes,and 2412 control probes. Each probe was selected to represent a 1 Kbinterval of the human genome. Because of the natural intersection ofepigenetically interesting loci (i.e. promoters, CpG Islands, etc) thereare multiple probes per genomic interval providing the capacity ofsupporting measurements with adjacent feature's data. The genomiccontent represented by the features represents the majority of ENSEMBLrecognized human transcriptional start sites (TSS) with two probes perTSS (>55,000 probes). In addition there are more than 35,000 probesdesigned to informatically identified CpG Islands (see, Takai and Jones,Proc Natl. Acad. Sci. U.S.A. 99(6):3740-3745 (2002)). In addition, morethan 7000 probes are dedicated to tiling consensus cancer genes at 1probe kb of genomic sequence. There are high, low and middle repetitiouscopy number controls (HERV, line and sine) and the design included tilesof the mitochondrial genome and a consensus rDNA gene.

Following statistical analysis of these datasets, loci that werepredicted to be differentially methylated in at least 70% of tumorsrelative to normal tissues were identified. As described in the Examplesbelow, differential DNA methylation of a collection of loci identifiedby a microarray discovery experiment was verified within the discoverypanel of tumor and normal samples, as well as validated in larger panelsof independent cancer tissue DNA, normal DNA tissue samples, and normalperipheral blood samples. Tables 1 and 2 and the section “SEQUENCELISTING” list the unique microarray feature identifier (Feature name)for each of these loci.

The genomic region in which a given microarray feature can report DNAmethylation status is dependent upon the molecular size of the DNAfragments that were labeled for the microarray hybridizations. Asdescribed above, DNA in the size range of 1 to 4 kb was purified byagarose gel extraction and used as template for cyanogen dye labeling.Therefore, a conservative estimate for the genomic region interrogatedby each microarray feature is 1 kb (i.e., 500 bp upstream and 500 bpdownstream of the sequence represented by the microarray feature). Notethat some features represent loci in which there is no Ensembl gene IDand no annotated transcribed gene within this 1 kb “wingspan” (e.g.,CHR01P063154999, CHR03P027740753, CHR10P118975684, CHR11P021861414,CHR14P093230340, ha1p_(—)12601_(—)150, ha1p_(—)42350_(—)150, andha1p_(—)44897_(—)150) and some features have Ensembl gene IDs but nogene description (e.g., CHR01P043164342, CHR01P225608458,CHR02P223364582, CHR03P052525960, CHR16P000373719, CHR19P018622408).Also note that some features represent loci in which more than oneEnsembl gene IDs within wingspan (e.g., CHR01P204123050,CHR02P223364582, and CHR16P066389027). DNA methylation at these loci maypotentially affect the regulation of any of these neighboring genes.

TABLE 1 Microarray Features Reporting Differential Methylation andIdentity of Annotated Genes within 1 kb of Each Feature Locus NumberFeature Name Ensembl Gene ID Annotations 1 CHR01P001976799ENSG00000067606 Protein kinase C, zeta type (EC 2.7.1.37) (nPKC-zeta).[Source: Uniprot/SWISSPROT; Acc: Q05513] 2 CHR01P026794862ENSG00000175793 14-3-3 protein sigma (Stratifin) (Epithelial cell markerprotein 1). [Source: Uniprot/SWISSPROT; Acc: P31947] 3 CHR01P043164342ENSG00000184157 no desc 4 CHR01P063154999 N/A N/A 5 CHR01P204123050ENSG00000162891 Interleukin-20 precursor (IL-20) (Four alpha helixcytokine Zcyto10). [Source: Uniprot/SWISSPROT; Acc: Q9NYY1]ENSG00000162896 Polymeric-immunoglobulin receptor precursor (Poly-Igreceptor) (PIGR) [Contains: Secretory component]. [Source:Uniprot/SWISSPROT; Acc: P01833] 6 CHR01P206905110 ENSG00000196878Laminin beta-3 chain precursor (Laminin 5 beta 3) (Laminin B1k chain)(Kalinin B1 chain). [Source: Uniprot/SWISSPROT; Acc: Q13751] 7CHR01P225608458 ENSG00000198504 no desc 8 CHR02P005061785ENSG00000171853 Tetratricopeptide repeat protein 15 (TPR repeat protein15). [Source: Uniprot/SWISSPROT; Acc: Q8WVT3] 9 CHR02P042255672ENSG00000162878 no desc 10 CHR02P223364582 ENSG00000135903 Paired boxprotein Pax-3 (HUP2). [Source: Uniprot/SWISSPROT; Acc: P23760]ENSG00000163081 no desc 11 CHR03P027740753 N/A N/A 12 CHR03P052525960ENSG00000168268 no desc 13 CHR03P069745999 ENSG00000187098Microphthalmia-associated transcription factor. [Source:Uniprot/SWISSPROT; Acc: O75030] 14 CHR05P059799713 ENSG00000152931Prostate-specific and androgen regulated protein PART-1. [Source:Uniprot/SWISSPROT; Acc: Q9NPD0] 15 CHR05P059799813 ENSG00000152931Prostate-specific and androgen regulated protein PART-1. [Source:Uniprot/SWISSPROT; Acc: Q9NPD0] 16 CHR05P177842690 ENSG00000050767collagen, type XXIII, alpha 1 [Source: RefSeq_peptide; Acc: NP_775736]17 CHR06P010694062 ENSG00000111846 N-acetyllactosaminidebeta-1,6-N-acetylglucosaminyl- transferase (EC 2.4.1.150)(N-acetylglucosaminyltransferase) (I-branching enzyme) (IGNT). [Source:Uniprot/SWISSPROT; Acc: Q06430] 18 CHR06P026333318 ENSG00000196966Histone H3.1 (H3/a) (H3/c) (H3/d) (H3/f) (H3/h) (H3/i) (H3/j) (H3/k)(H3/l). [Source: Uniprot/SWISSPROT; Acc: P68431] 19 CHR08P102460854ENSG00000083307 transcription factor CP2-like 3 [Source: RefSeq_peptide;Acc: NP_079191] 20 CHR08P102461254 ENSG00000083307 transcription factorCP2-like 3 [Source: RefSeq_peptide; Acc: NP_079191] 21 CHR08P102461554ENSG00000083307 transcription factor CP2-like 3 [Source: RefSeq_peptide;Acc: NP_079191] 22 CHR09P000107988 ENSG00000184492 Forkhead box proteinD4 (Forkhead-related protein FKHL9) (Forkhead-related transcriptionfactor 5) (FREAC-5) (Myeloid factor-alpha). [Source: Uniprot/SWISSPROT;Acc: Q12950] 23 CHR09P021958839 ENSG00000147889 Cyclin-dependent kinase4 inhibitor A (CDK4I) (p16-INK4) (p16-INK4a) (Multiple tumorsuppressor 1) (MTS1). [Source: Uniprot/SWISSPROT; Acc: P42771] 24CHR09P131048752 ENSG00000165699 Hamartin (Tuberous sclerosis 1 protein).[Source: Uniprot/SWISSPROT; Acc: Q92574] 25 CHR10P118975684 N/A N/A 26CHR11P021861414 N/A N/A 27 CHR12P004359362 ENSG00000118972 Fibroblastgrowth factor 23 precursor (FGF-23) (Tumor- derivedhypophosphatemia-inducing factor). [Source: Uniprot/SWISSPROT; Acc:Q9GZV9] 28 CHR12P016001231 ENSG00000023697 Putativedeoxyribose-phosphate aldolase (EC 4.1.2.4) (Phosphodeoxyriboaldolase)(Deoxyriboaldolase) (DERA). [Source: Uniprot/SWISSPROT; Acc: Q9Y315] 29CHR14P018893344 ENSG00000185271 PREDICTED: similar to RIKEN cDNAC530050O22 [Source: RefSeq_peptide_predicted; Acc: XP_063481] 30CHR14P093230340 N/A N/A 31 CHR16P000373719 ENSG00000198098 no desc 32CHR16P066389027 ENSG00000089505 Chemokine-like factor (C32). [Source:Uniprot/SWISSPROT; Acc: Q9UBR5] ENSG00000140932 Chemokine-like factorsuper family member 2. [Source: Uniprot/SWISSPROT; Acc: Q8TAZ6] 33CHR16P083319654 ENSG00000140945 Cadherin-13 precursor(Truncated-cadherin) (T-cadherin) (T- cad) (Heart-cadherin) (H-cadherin)(P105). [Source: Uniprot/SWISSPROT; Acc: P55290] 34 CHR18P019705147ENSG00000053747 Laminin alpha-3 chain precursor (Epiligrin 170 kDasubunit) (E170) (Nicein alpha subunit). [Source: Uniprot/SWISSPROT; Acc:Q16787] 35 CHR19P018622408 ENSG00000167487 no desc 36 CHR19P051892823ENSG00000105287 Protein kinase C, D2 type (EC 2.7.1.—) (nPKC-D2)(Protein kinase D2). [Source: Uniprot/SWISSPROT; Acc: Q9BZL6] 37CHRXP013196410 ENSG00000046653 Neuronal membrane glycoprotein M6-b(M6b). [Source: Uniprot/SWISSPROT; Acc: Q13491] 38 CHRXP013196870ENSG00000046653 Neuronal membrane glycoprotein M6-b (M6b). [Source:Uniprot/SWISSPROT; Acc: Q13491] 39 ha1p16_00179_l50 ENSG00000147889Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4) (p16-INK4a)(Multiple tumor suppressor 1) (MTS1). [Source: Uniprot/SWISSPROT; Acc:P42771] 40 ha1p16_00182_l50 ENSG00000147889 Cyclin-dependent kinase 4inhibitor A (CDK4I) (p16-INK4) (p16-INK4a) (Multiple tumor suppressor 1)(MTS1). [Source: Uniprot/SWISSPROT; Acc: P42771] 41 ha1p16_00257_l50ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)(p16-INK4a) (Multiple tumor suppressor 1) (MTS1). [Source:Uniprot/SWISSPROT; Acc: P42771] 42 ha1p_12601_l50 N/A N/A 43ha1p_17147_l50 ENSG00000072201 Ubiquitin ligase LNX (EC 6.3.2.—)(Numb-binding protein 1) (Ligand of Numb-protein X 1). [Source:Uniprot/SWISSPROT; Acc: Q8TBB1] 44 ha1p_42350_l50 N/A N/A 45ha1p_44897_l50 N/A N/A 46 ha1p_61253_l50 ENSG00000168767 GlutathioneS-transferase Mu 2 (EC 2.5.1.18) (GSTM2-2) (GST class-mu 2). [Source:Uniprot/SWISSPROT; Acc: P28161] 47 chr01p001005050 N/A N/A 48chr16p001157479 ENSG00000196557 Voltage-dependent T-type calcium channelalpha-1H subunit (Voltage-gated calcium channel alpha subunit Cav3.2).[Source: Uniprot/SWISSPROT; Acc: O95180] 49 ha1g_00681 ENSG00000105997Homeobox protein Hox-A3 (Hox-1E). [Source: Uniprot/SWISSPROT; Acc:O43365] 50 ha1g_01966 N/A N/A 51 ha1g_02153 N/A N/A 52 ha1g_02319ENSG00000135638 Homeobox protein EMX1 (Empty spiracles homolog 1) (Emptyspiracles-like protein 1). [Source: Uniprot/SWISSPROT; Acc: Q04741] 53ha1g_02335 ENSG00000106006 Homeobox protein Hox-A6 (Hox-1B). [Source:Uniprot/SWISSPROT; Acc: P31267] 54 ha1p16_00182 ENSG00000147889Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4) (p16-INK4a)(Multiple tumor suppressor 1) (MTS1). [Source: Uniprot/SWISSPROT; Acc:P42771] 55 ha1p16_00185 ENSG00000147889 Cyclin-dependent kinase 4inhibitor A (CDK4I) (p16-INK4) (p16-INK4a) (Multiple tumor suppressor 1)(MTS1). [Source: Uniprot/SWISSPROT; Acc: P42771] 56 ha1p16_00193ENSG00000147889 Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4)(p16-INK4a) (Multiple tumor suppressor 1) (MTS1). [Source:Uniprot/SWISSPROT; Acc: P42771] 57 ha1p16_00259 ENSG00000147889Cyclin-dependent kinase 4 inhibitor A (CDK4I) (p16-INK4) (p16-INK4a)(Multiple tumor suppressor 1) (MTS1). [Source: Uniprot/SWISSPROT; Acc:P42771] 58 ha1p_02799 N/A N/A 59 ha1p_03567 ENSG00000165678 Growthhormone inducible transmembrane protein (Dermal papilla derived protein2) (Transmembrane BAX inhibitor motif containing protein 5). [Source:Uniprot/SWISSPROT; Acc: Q9H3K2] 60 ha1p_03671 ENSG00000158195Wiskott-Aldrich syndrome protein family member 2 (WASP- family proteinmember 2) (WAVE-2 protein) (Verprolin homology domain-containing protein2). [Source: Uniprot/SWISSPROT; Acc: Q9Y6W5] 61 ha1p_05803 N/A N/A 62ha1p_07131 N/A N/A 63 ha1p_07989 ENSG00000066032 Alpha-2 catenin(Alpha-catenin-related protein) (Alpha N- catenin). [Source:Uniprot/SWISSPROT; Acc: P26232] ENSG00000181987 no desc 64 ha1p_08588N/A N/A 65 ha1p_09700 ENSG00000171243 Sclerostin domain containingprotein 1 precursor (Ectodermal BMP inhibitor) (Ectodin) (Uterinesensitization-associated gene 1 protein) (USAG-1). [Source:Uniprot/SWISSPROT; Acc: Q6X4U4] 66 ha1p_104458 N/A N/A 67 ha1p_105287ENSG00000089356 FXYD domain-containing ion transport regulator 3precursor (Chloride conductance inducer protein Mat-8) (Mammary tumor 8kDa protein) (Phospholemman-like). [Source: Uniprot/SWISSPROT; Acc:Q14802] 68 ha1p_10702 ENSG00000105996 Homeobox protein Hox-A2. [Source:Uniprot/SWISSPROT; Acc: O43364] 69 ha1p_108469 ENSG00000099337 Potassiumchannel subfamily K member 6 (Inward rectifying potassium channelprotein TWIK-2) (TWIK-originated similarity sequence). [Source:Uniprot/SWISSPROT; Acc: Q9Y257] 70 ha1p_108849 ENSG00000083844 Zincfinger protein 264. [Source: Uniprot/SWISSPROT; Acc: O43296] 71ha1p_11016 ENSG00000106125 Aquaporin-1 (AQP-1) (Aquaporin-CHIP) (Waterchannel protein for red blood cells and kidney proximal tubule) (Urinewater channel). [Source: Uniprot/SWISSPROT; Acc: P29972] 72 ha1p_11023ENSG00000154978 EGFR-coamplified and overexpressed protein [Source:RefSeq_peptide; Acc: NP_110423] 73 ha1p_12974 ENSG00000154277 Ubiquitincarboxyl-terminal hydrolase isozyme L1 (EC 3.4.19.12) (EC 6.—.—.—)(UCH-L1) (Ubiquitin thiolesterase L1) (Neuron cytoplasmic protein 9.5)(PGP 9.5) (PGP9.5). [Source: Uniprot/SWISSPROT; Acc: P09936] 74ha1p_16027 ENSG00000170178 Homeobox protein Hox-D12 (Hox-4H). [Source:Uniprot/SWISSPROT; Acc: P35452] 75 ha1p_16066 ENSG00000128709 Homeoboxprotein Hox-D9 (Hox-4C) (Hox-5.2). [Source: Uniprot/SWISSPROT; Acc:P28356] 76 ha1p_18911 ENSG00000115306 Spectrin beta chain, brain 1(Spectrin, non-erythroid beta chain 1) (Beta-II spectrin) (Fodrin betachain). [Source: Uniprot/SWISSPROT; Acc: Q01082] 77 ha1p_19254ENSG00000149571 Kin of IRRE-like protein 3 precursor (Kin of irregularchiasm- like protein 3) (Nephrin-like 2). [Source: Uniprot/SWISSPROT;Acc: Q8IZU9] 78 ha1p_19853 ENSG00000186960 Full-length cDNA cloneCS0DF012YF04 of Fetal brain of Homo sapiens (human) (Fragment). [Source:Uniprot/SPTREMBL; Acc: Q86U37] 79 ha1p_22257 ENSG00000001626 Cysticfibrosis transmembrane conductance regulator (CFTR) (cAMP-dependentchloride channel). [Source: Uniprot/SWISSPROT; Acc: P13569] 80ha1p_22519 N/A N/A 81 ha1p_31800 N/A N/A 82 ha1p_33290 ENSG00000147408Chondroitin beta-1,4-N-acetylgalactosaminyltransferase 1 (EC 2.4.1.174)(beta4GalNAcT-1). [Source: Uniprot/SWISSPROT; Acc: Q8TDX6] 83 ha1p_37635ENSG00000066405 Claudin-18. [Source: Uniprot/SWISSPROT; Acc: P56856] 84ha1p_39189 ENSG00000121853 Growth hormone secretagogue receptor type 1(GHS-R) (GH- releasing peptide receptor) (GHRP) (Ghrelin receptor).[Source: Uniprot/SWISSPROT; Acc: Q92847] 85 ha1p_39511 ENSG00000164035Endomucin precursor (Endomucin-2) (Gastric cancer antigen Ga34).[Source: Uniprot/SWISSPROT; Acc: Q9ULC0] 86 ha1p_39752 ENSG00000169836Neuromedin K receptor (NKR) (Neurokinin B receptor) (NK- 3 receptor)(NK-3R) (Tachykinin receptor 3). [Source: Uniprot/SWISSPROT; Acc:P29371] 87 ha1p_60945 ENSG00000070814 Treacle protein (Treacher Collinssyndrome protein). [Source: Uniprot/SWISSPROT; Acc: Q13428] 88ha1p_62183 N/A N/A 89 ha1p_69418 ENSG00000180667 no desc 90 ha1p_71224ENSG00000113205 Protocadherin beta 3 precursor (PCDH-beta3). [Source:Uniprot/SWISSPROT; Acc: Q9Y5E6] 91 ha1p_74221 ENSG00000125895 no desc 92ha1p_76289 ENSG00000145888 Glycine receptor alpha-1 chain precursor(Glycine receptor 48 kDa subunit) (Strychnine binding subunit). [Source:Uniprot/SWISSPROT; Acc: P23415] 93 ha1p_81050 ENSG00000187529 PREDICTED:similar to 60S ribosomal protein L7 [Source: RefSeq_peptide_predicted;Acc: XP_018432] 94 ha1p_81674 ENSG00000174197 MGA protein (Fragment).[Source: Uniprot/SPTREMBL; Acc: Q81WI9] 95 ha1p_86355 ENSG00000171878Ferritin light chain (Fragment). [Source: Uniprot/SPTREMBL; Acc: Q6DMM8]96 ha1p_98491 N/A N/A 97 ha1p_99426 ENSG00000198028 zinc finger protein560 [Source: RefSeq_peptide; Acc: NP_689689]

Example 2 Design of Independent DNA Methylation Verification andValidation Assays

PCR primers that interrogated the loci predicted to be differentiallymethylated between tumor and histologically normal tissue were designed.Due to the functional properties of the enzyme, DNAmethylation-dependent depletion of DNA fragments by McrBC is capable ofmonitoring the DNA methylation status of sequences neighboring thegenomic sequences represented by the features on the microarraydescribed in Example 1 (wingspan). Since the size of DNA fragmentsanalyzed as described in Example 1 was approximately 1-4 kb, we selecteda 1 kb region spanning the sequence represented by the microarrayfeature as a conservative estimate of the predicted region ofdifferential methylation. For each locus, PCR primers were selectedwithin this approximately 1 kb region flanking the genomic sequencerepresented on the DNA microarray (approximately 500 bp upstream and 500bp downstream). Selection of primer sequences was guided by uniquenessof the primer sequence across the genome, as well as the distribution ofpurine-CG sequences within the 1 kb region. PCR primer pairs wereselected to amplify an approximately 400-600 bp sequence within each 1kb region. Optimal PCR cycling conditions for the primer pairs wereempirically determined, and amplification of a specific PCR amplicon ofthe correct size was verified. The sequences of the microarray features,primer pairs and amplicons are indicated in Table 2, and in section“SEQUENCE LISTING.”

TABLE 2 Sequence identification numbers for all sequences described inthe application. See, section “SEQUENCE LISTING” for actual sequences aslisted by number in the table. Locus Left Right Amplicon DNA RegionNumber Primer Primer Seq. Seq. (SEQ (SEQ ID (SEQ ID Annealing (SEQ ID(SEQ ID Feature Name ID NO:) NO:) NO:) Temp. NO:) NO:) CHR01P001976799 198 195 66 C. 292 389 CHR01P026794862 2 99 196 62 C. 293 390CHR01P043164342 3 100 197 66 C. 294 391 CHR01P063154999 4 101 198 66 C.295 392 CHR01P204123050 5 102 199 62 C. 296 393 CHR01P206905110 6 103200 66 C. 297 394 CHR01P225608458 7 104 201 66 C. 298 395CHR02P005061785 8 105 202 72 C. 299 396 CHR02P042255672 9 106 203 66 C.300 397 CHR02P223364582 10 107 204 66 C. 301 398 CHR03P027740753 11 108205 66 C. 302 399 CHR03P052525960 12 109 206 66 C. 303 400CHR03P069745999 13 110 207 66 C. 304 401 CHR05P059799713 14 111 208 66C. 305 402 CHR05P059799813 15 112 209 66 C. 306 403 CHR05P177842690 16113 210 62 C. 307 404 CHR06P010694062 17 114 211 66 C. 308 405CHR06P026333318 18 115 212 66 C. 309 406 CHR08P102460854 19 116 213 66C. 310 407 CHR08P102461254 20 117 214 66 C. 311 408 CHR08P102461554 21118 215 66 C. 312 409 CHR09P000107988 22 119 216 66 C. 313 410CHR09P021958839 23 120 217 66 C. 314 411 CHR09P131048752 24 121 218 66C. 315 412 CHR10P118975684 25 122 219 66 C. 316 413 CHR11P021861414 26123 220 66 C. 317 414 CHR12P004359362 27 124 221 66 C. 318 415CHR12P016001231 28 125 222 66 C. 319 416 CHR14P018893344 29 126 223 66C. 320 417 CHR14P093230340 30 127 224 66 C. 321 418 CHR16P000373719 31128 225 66 C. 322 419 CHR16P066389027 32 129 226 66 C. 323 420CHR16P083319654 33 130 227 66 C. 324 421 CHR18P019705147 34 131 228 66C. 325 422 CHR19P018622408 35 132 229 66 C. 326 423 CHR19P051892823 36133 230 66 C. 327 424 CHRXP013196410 37 134 231 66 C. 328 425CHRXP013196870 38 135 232 66 C. 329 426 ha1p16_00179_l50 39 136 233 66C. 330 427 ha1p16_00182_l50 40 137 234 66 C. 331 428 ha1p16_00257_l50 41138 235 66 C. 332 429 ha1p_12601_l50 42 139 236 66 C. 333 430ha1p_17147_l50 43 140 237 66 C. 334 431 ha1p_42350_l50 44 141 238 66 C.335 432 ha1p_44897_l50 45 142 239 66 C. 336 433 ha1p_61253_l50 46 143240 72 C. 337 434 CHR01P001005050 47 144 241 72 C. 338 435CHR16P001157479 48 145 242 72 C. 339 436 ha1g_00681 49 146 243 65 C. 340437 ha1g_01966 50 147 244 65 C. 341 438 ha1g_02153 51 148 245 65 C. 342439 ha1g_02319 52 149 246 65 C. 343 440 ha1g_02335 53 150 247 65 C. 344441 ha1p16_00182 54 151 248 65 C. 345 442 ha1p16_00185 55 152 249 65 C.346 443 ha1p16_00193 56 153 250 65 C. 347 444 ha1p16_00259 57 154 251 65C. 348 445 ha1p_02799 58 155 252 65 C. 349 446 ha1p_03567 59 156 253 65C. 350 447 ha1p_03671 60 157 254 65 C. 351 448 ha1p_05803 61 158 255 65C. 352 449 ha1p_07131 62 159 256 65 C. 353 450 ha1p_07989 63 160 257 65C. 354 451 ha1p_08588 64 161 258 65 C. 355 452 ha1p_09700 65 162 259 65C. 356 453 ha1p_104458 66 163 260 65 C. 357 454 ha1p_105287 67 164 26165 C. 358 455 ha1p_10702 68 165 262 65 C. 359 456 ha1p_108469 69 166 26365 C. 360 457 ha1p_108849 70 167 264 65 C. 361 458 ha1p_11016 71 168 26565 C. 362 459 ha1p_11023 72 169 266 65 C. 363 460 ha1p_12974 73 170 26765 C. 364 461 ha1p_16027 74 171 268 65 C. 365 462 ha1p_16066 75 172 26965 C. 366 463 ha1p_18911 76 173 270 65 C. 367 464 ha1p_19254 77 174 27165 C. 368 465 ha1p_19853 78 175 272 65 C. 369 466 ha1p_22257 79 176 27365 C. 370 467 ha1p_22519 80 177 274 65 C. 371 468 ha1p_31800 81 178 27565 C. 372 469 ha1p_33290 82 179 276 65 C. 373 470 ha1p_37635 83 180 27765 C. 374 471 ha1p_39189 84 181 278 65 C. 375 472 ha1p_39511 85 182 27965 C. 376 473 ha1p_39752 86 183 280 65 C. 377 474 ha1p_60945 87 184 28165 C. 378 475 ha1p_62183 88 185 282 65 C. 379 476 ha1p_69418 89 186 28365 C. 380 477 ha1p_71224 90 187 284 65 C. 381 478 ha1p_74221 91 188 28565 C. 382 479 ha1p_76289 92 189 286 65 C. 383 480 ha1p_81050 93 190 28765 C. 384 481 ha1p_81674 94 191 288 65 C. 385 482 ha1p_86355 95 192 28965 C. 386 483 ha1p_98491 96 193 290 65 C. 387 484 ha1p_99426 97 194 29165 C. 388 485

Example 3 Verification of Microarray DNA Methylation Predictions

Initially, the DNA methylation state of the loci was independentlyassayed in 10 ovarian carcinoma samples and the 10 histologically normalsamples described above (i.e. the discovery tissue panel used formicroarray experiments). DNA methylation was assayed by a quantitativePCR approach utilizing digestion by the McrBC restriction enzyme tomonitor DNA methylation status. Genomic DNA purified from each samplewas split into two equal portions of 9.6 μg. One 9.6 μg portion (TreatedPortion) was digested with McrBC in a total volume of 120 μL including1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serum albumin (NewEngland Biolabs), 2 mM GTP (Roche) and 80 units of McrBC enzyme (NewEngland Biolabs). The second 9.6 μg portion (Untreated Portion) wastreated exactly the same as the Treated Portion, except that 8 μL ofsterile 50% glycerol was added instead of McrBC enzyme. Reactions wereincubated at 37° C. for approximately 12 hours, followed by incubationat 60° C. for 20 minutes to inactivate McrBC.

The extent of McrBC cleavage at each locus was monitored by quantitativereal-time PCR (qPCR). For each assayed locus, qPCR was performed using20 ng of the Untreated Portion DNA as template and, separately, using 20ng of the Treated Portion DNA as template. Each reaction was performedin 10 μL total volume including 1× LightCycler 480 SYBR Green I Mastermix (Roche) and 625 nM of each primer. Reactions were run in a RocheLightCycler 480 instrument. Optimal annealing temperatures varieddepending on the primer pair. Primer sequences (Left Primer; RightPrimer) and appropriate annealing temperatures (Annealing Temp.) areshown in Table 2. Cycling conditions were: 95° C. for 5 min.; 45 cyclesof 95° C. for 1 min., [annealing temperature, see Table 2] for 30 sec.,72° C. for 1 min., 83° C. for 2 sec. followed by a plate read. Meltingcurves were calculated under the following conditions: 95° C. for 5sec., 65° C. for 1 min., 65° C. to 95° C. at 2.5° C./sec. ramp rate withcontinuous plate reads. Each Untreated/Treated qPCR reaction pair wasperformed in duplicate. The difference in the cycle number at whichamplification crossed threshold (delta Ct) was calculated for eachUntreated/Treated qPCR reaction pair by subtracting the Ct of theUntreated Portion from the Ct of the Treated Portion. BecauseMcrBC-mediated cleavage between the two primers increases the Ct of theTreated Portion, increasing delta Ct values reflect increasingmeasurements of local DNA methylation densities. The average delta Ctbetween the two replicate Untreated/Treated qPCR reactions wascalculated, as well as the standard deviation between the two delta Ctvalues.

Example 4 Validation of DNA Methylation Changes in Larger Number ofIndependent Ovarian Tumor, Normal Ovarian Samples, and Normal BloodSamples

The differential DNA methylation status of the loci was furthervalidated by analyzing an independent panel of 26 ovarian carcinomasamples (17 Stage 1 and 9 Stage II), 27 normal ovarian tissue samples,and 23 normal blood samples. The normal ovarian tissues included in thispanel were obtained from mastectomies unrelated to ovarian cancer. Eachsample was split into two equal portions of 4 μg. One portion wasdigested with McrBC (Treated Portion) in a total volume of 200 μLincluding 1×NEB2 buffer (New England Biolabs), 0.1 mg/mL bovine serumalbumin (New England Biolabs), 2 mM GTP (Roche) and 32 units McrBC (NewEngland Biolabs). The second portion was mock treated under identicalconditions, except that 3.2 μL sterile 50% glycerol was added instead ofMcrBC enzyme (Untreated Portion). Samples were incubated at 37° C. forapproximately 12 hours, followed by incubation at 60° C. to inactivatethe McrBC enzyme. qPCR reactions and data analysis were performed asdescribed in Example 3.

Table 3 indicates the percent sensitivity and specificity for eachlocus. Gain biomarkers are biomarkers that show more methylation intumor samples than normal samples and loss biomarkers show conversely.For gain biomarkers, sensitivity reflects the frequency of scoring aknown tumor sample as positive for DNA methylation at each locus whilespecificity reflects the frequency of scoring a known normal sample asnegative for DNA methylation at each locus. For loss biomarkers,sensitivity reflects the frequency of scoring a known tumor sample asnegative for DNA methylation at each locus while specificity reflectsthe frequency of scoring a known normal sample as positive for DNAmethylation at each locus. As described above, an average delta Ct>1.0(Treated Portion—Untreated Portion) was used as a threshold to score asample as positive for DNA methylation at each locus (representing >50%depletion of amplifiable molecules in the DNA methylation-dependentrestricted population relative to the untreated population). Percentsensitivity of gain biomarkers was calculated as the number of tumorsamples with an average delta Ct>1.0 divided by the total number oftumor samples analyzed for that locus (i.e. excluding any measurementswith a standard deviation between qPCR replicates>1 cycle)×100. Percentspecificity of gain biomarkers was calculated as (1−(the number ofnormal samples with an average delta Ct>1.0 divided by the total numberof normal samples analyzed for that locus))×100. On the contrary percentsensitivity and specificity of loss biomarkers was calculated viceversa. As shown in Table 3, the loci have sensitivities>8% andspecificities relative to normal ovarian samples>40%. Notably, at least9 of the loci have 100% specificity relative to normal ovarian andrelative to normal blood samples. It is important to point out that thesensitivity and specificity of the differential DNA methylation statusof any given locus may be increased by further optimization of theprecise local genetic region interrogated by a DNA methylation-sensingassay.

TABLE 3 Sensitivity and Specificity of Differentially Methylated Loci ina Panel of 26 Ovarian Tumor Samples, 27 Normal Ovarian Samples, and 23Normal Blood Samples Specificity Locus vs Normal Specificity FeatureName No. Sensitivity Ovary vs Normal Blood CHR01P001976799 1 96% 100% 0%CHR01P026794862 2 43% 60% 89% CHR01P043164342 3 42% 100% 100%CHR01P063154999 4 85% 100% 100% CHR01P204123050 5 76% 42% 5%CHR01P206905110 6 38% 100% 100% CHR01P225608458 7 85% 63% 4%CHR02P005061785 8 100% 89% 0% CHR02P042255672 9 81% 100% 0%CHR02P223364582 10 92% 52% 100% CHR03P027740753 11 77% 100% 100%CHR03P052525960 12 85% 67% 0% CHR03P069745999 13 8% 100% 100%CHR05P059799713 14 42% 100% 100% CHR05P059799813 15 35% 100% 100%CHR05P177842690 16 62% 96% 95% CHR06P010694062 17 88% 63% 0%CHR06P026333318 18 96% 89% 0% CHR08P102460854 19 84% 93% 0%CHR08P102461254 20 76% 96% 0% CHR08P102461554 21 80% 96% 0%CHR09P000107988 22 73% 96% 91% CHR09P021958839 23 88% 92% 90%CHR09P131048752 24 96% 96% 0% CHR10P118975684 25 35% 100% 0%CHR11P021861414 26 19% 100% 100% CHR12P004359362 27 38% 96% 87%CHR12P016001231 28 50% 96% 80% CHR14P018893344 29 85% 96% 0%CHR14P093230340 30 92% 89% 100% CHR16P000373719 31 38% 100% 0%CHR16P066389027 32 15% 96% 100% CHR16P083319654 33 38% 93% 87%CHR18P019705147 34 31% 100% 100% CHR19P018622408 35 92% 100% 0%CHR19P051892823 36 78% 89% 10% CHRXP013196410 37 96% 83% 14%CHRXP013196870 38 96% 80% 9% ha1p16_00179_l50 39 88% 96% 100%ha1p16_00182_l50 40 81% 96% 100% ha1p16_00257_l50 41 81% 86% 100%ha1p_12601_l50 42 69% 100% 0% ha1p_17147_l50 43 56% 100% 13%ha1p_42350_l50 44 43% 96% 0% ha1p_44897_l50 45 96% 40% 0% ha1p_61253_l5046 71% 95% 0% CHR01P001005050 47 76% 100% 100% CHR16P001157479 48 29%100% 100%

Example 5 Validation of DNA Methylation Changes in Independent LungTumor, Normal Lung Samples and in Normal Peripheral Blood Samples

The differential DNA methylation status of the 49 loci was validated byanalyzing an independent panel of 4 lung non-small adenocarcinomasamples (1 Stage I, 1 Stage II, 1 Stage III, 1 Stage 1V) and 4 matchedadjacent histologically normal as well as in 23 samples of peripheralblood of normal individuals. Each sample was split into two equalportions of 4 μg. One portion was digested with McrBC (Treated Portion)in a total volume of 200 μL including 1×NEB2 buffer (New EnglandBiolabs), 0.1 mg/mL bovine serum albumin (New England Biolabs), 2 mM GTP(Roche) and 32 units McrBC (New England Biolabs). The second portion wasmock treated under identical conditions, except that 3.2 μL sterile 50%glycerol was added instead of McrBC enzyme (Untreated Portion). Sampleswere incubated at 37° C. for approximately 12 hours, followed byincubation at 60° C. to inactivate the McrBC enzyme. qPCR reactions anddata analysis were performed as described in these Examples.

Table 4 indicates the percent sensitivity and specificity for eachlocus. Gain biomarkers are biomarkers that show more methylation intumor samples than normal samples and loss biomarkers show conversely.For gain biomarkers, sensitivity reflects the frequency of scoring aknown tumor sample as positive for DNA methylation at each locus whilespecificity reflects the frequency of scoring a known normal sample asnegative for DNA methylation at each locus. For loss biomarkers,sensitivity reflects the frequency of scoring a known tumor sample asnegative for DNA methylation at each locus while specificity reflectsthe frequency of scoring a known normal sample as positive for DNAmethylation at each locus. As described above, an average delta Ct>1.0(Treated Portion—Untreated Portion) was used as a threshold to score asample as positive for DNA methylation at each locus (representing >50%depletion of amplifiable molecules in the DNA methylation-dependentrestricted population relative to the untreated population). Percentsensitivity of gain biomarkers was calculated as the number of tumorsamples with an average delta Ct>1.0 divided by the total number oftumor samples analyzed for that locus (i.e. excluding any measurementswith a standard deviation between qPCR replicates>1 cycle)×100. Percentspecificity of gain biomarkers was calculated as (1−(the number ofnormal samples with an average delta Ct>1.0 divided by the total numberof normal samples analyzed for that locus))×100. On the contrary percentsensitivity and specificity of loss biomarkers was calculated viceversa. As shown in Table 4, the 49 loci have sensitivities>32% up to100% and specificities in range 8-100% in tissues and in range 0-100%specificity in peripheral blood. Notably, 33 of the 49 loci have 100%specificity in tissues, and 19 of the 49 loci have 100% specificity inblood. It is important to point out that the sensitivity and specificityof the differential DNA methylation status of any given locus may beincreased by further optimization of the precise local genetic regioninterrogated by a DNA methylation-sensing assay.

TABLE 4 Sensitivity and specificity of Differentially Methylated Loci ina Panel Of 13 Adjacent Normal Lung Samples, 13 Lung Tumor Samples and 23Normal Blood Samples Locus Specificity Feature Name Number SensitivityAdj.normal Blood ha1g_00681 49 58% 100% 0% ha1g_01966 50 75% 100% 95%ha1g_02153 51 67% 100% 100% ha1g_02319 52 50% 100% 100% ha1g_02335 5364% 100% 0% ha1p_02799 58 38% 100% 95% ha1p_03567 59 77% 85% 89%ha1p_03671 60 33% 100% 100% ha1p_05803 61 77% 92% 23% ha1p_07131 62 38%100% 100% ha1p_07989 63 62% 100% 100% ha1p_08588 64 54% 100% 100%ha1p_09700 65 38% 100% 94% ha1p_104458 66 100% 77% 0% ha1p_105287 67 69%100% 100% ha1p_10702 68 62% 100% 80% ha1p_108469 69 62% 100% 100%ha1p_108849 70 92% 31% 0% ha1p_11016 71 100% 15% 0% ha1p_11023 72 92% 8%26% ha1p_12974 73 46% 100% 100% ha1p_16027 74 54% 100% 100% ha1p_1606675 85% 100% 95% ha1p_18911 76 100% 23% 95% ha1p_19254 77 77% 100% 44%ha1p_19853 78 69% 100% 100% ha1p_22257 79 64% 100% 91% ha1p_22519 80 77%92% 91% ha1p_31800 81 100% 31% 50% ha1p_33290 82 83% 92% 50% ha1p_3763583 91% 100% 45% ha1p_39189 84 50% 100% 100% ha1p_39511 85 100% 27% 31%ha1p_39752 86 75% 77% 85% ha1p_60945 87 85% 15% 21% ha1p_62183 88 50%100% 95% ha1p_69418 89 75% 100% 100% ha1p_71224 90 92% 83% 67%ha1p_74221 91 64% 100% 42% ha1p_76289 92 64% 100% 90% ha1p_81050 93 54%100% 100% ha1p_81674 94 83% 92% 100% ha1p_86355 95 69% 83% 94%ha1p_98491 96 54% 100% 100% ha1p_99426 97 62% 100% 100% ha1p16_00182 5438% 100% 94% ha1p16_00185 55 38% 100% 100% ha1p16_00193 56 92% 77% 100%ha1p16_00259 57 82% 85% 95%

Example 6 Further Validation of Selected DNA Methylation Biomarkers in aLarger Panel of Lung Tumor Samples and Normal Lung Samples

A panel of 37 loci were selected for further validation in a panel of 25additional lung carcinoma samples as well as 25 additional matchedadjacent histologically normal samples, bringing the total number oftumor and normal samples analyzed to 38. The panel also included 22 lungsamples from individuals who died from reasons other than cancer (i.e.,benign samples). Samples were treated and analyzed as described in theseExamples. As shown in Table 5, these loci display greater than 19%sensitivity, and all of them showed greater than 70% specificityrelative to normal lung tissue, and 30 showed greater than 90%specificity relative to normal peripheral blood.

To address the applicability of the differential DNA methylation eventsas biomarkers for additional tumor types, a subset of claimed loci wereanalyzed in a panel of 10 cervical tumor samples and 8 benign cervicalsamples. All tumors were squamous cell cervical carcinomas withhistology-confirmed neoplastic cellularity ranging from 75% to 95%.Benign cervical samples were obtained from hysterectomies of cervicalcancer-free women. Some loci listed in Table 5 were analyzed using thesame qPCR based assays as described in Example 3. Receiver-operatorcharacteristic analysis (Lasko, et al. (2005) Journal of BiomedicalInformatics 38(5):404-415.) was used to determine empirical thresholdvalues for classification of tissue samples. The analysis was performedindependently for each locus. Resulting sensitivity and specificitycalculations are shown in Table 5 (columns labeled “cervical”). Theseresults demonstrate that loci discovered to be differentially methylatedin lung tumors relative to normal or benign tissue can also be relevantbiomarkers of cancers other than lung cancer.

TABLE 5 Sensitivity and specificity of differentially methylated loci ina panel of 22 benign lung samples, 38 adjacent normal samples, 38 lungtumor samples and 23 normal blood samples using ROC analysis Adjacent &Benign Adjacent Normal Benign Samples Blood Cervical Feature NameSensitivity Specificity Sensitivity Specificity Sensitivity SpecificitySensitivity Specificity Sensitivity Specificity ha1g_01966 84.21% 85.00%84.21% 84.21% 89.47% 90.91% 84.21% 82.61% 90.00% 87.50% ha1g_0215378.95% 78.33% 78.95% 78.95% 78.95% 77.27% 86.84% 100.00% 100.00% 100.00%  ha1g_02319 84.21% 85.00% 78.95% 81.58% 86.84% 86.36% 94.74%95.65% 90.00% 87.50% ha1p_02799 60.53% 60.00% 60.53% 60.53% 60.53%59.09% 81.58% 82.61% — — ha1p_03567 84.21% 85.00% 81.58% 81.58% 86.84%86.36% 78.95% 78.26% — — ha1p_03671 65.79% 68.33% 65.79% 68.42% 65.79%68.18% 78.95% 78.26% 80.00% 75.00% ha1p_07131 89.47% 88.33% 86.84%86.84% 92.11% 90.91% 94.74% 95.65% 70.00% 75.00% ha1p_07989 84.21%85.00% 84.21% 84.21% 86.84% 86.36% 84.21% 82.61% — — ha1p_08588 84.21%85.00% 84.21% 81.58% 86.84% 86.36% 86.84% 86.96% 60.00% 62.50%ha1p_09700 68.42% 68.33% 68.42% 68.42% 68.42% 68.18% 63.16% 65.22% — —ha1p_105287 78.95% 80.00% 78.95% 78.95% 84.21% 86.36% 94.74% 95.65%70.00% 75.00% ha1p_10702 63.16% 66.67% 63.16% 73.68% 63.16% 63.64%63.16% 78.26% — — ha1p_108469 78.95% 78.33% 81.58% 81.58% 78.95% 77.27%86.84% 86.96% 70.00% 62.50% ha1p_12974 57.89% 58.33% 52.63% 52.63%65.79% 68.18% 76.32% 100.00% 80.00% 75.00% ha1p_16027 78.95% 78.33%76.32% 76.32% 86.84% 86.36% 100.00% 100.00% 70.00% 75.00% ha1p_1606678.95% 80.00% 78.95% 78.95% 81.58% 81.82% 92.11% 91.30% — — ha1p_1891173.68% 73.33% 73.68% 73.68% 73.68% 72.73% 76.32% 78.26% 60.00% 62.50%ha1p_19853 86.84% 86.67% 86.84% 86.84% 86.84% 86.36% 92.11% 91.30%90.00% 87.50% ha1p_22257 76.32% 76.67% 78.95% 78.95% 68.42% 68.18%73.68% 73.91% — — ha1p_22519 84.21% 83.33% 81.58% 81.58% 84.21% 86.36%68.42% 69.57% — — ha1p_31800 84.21% 83.33% 89.47% 86.84% 81.58% 81.82%60.53% 60.87% — — ha1p_33290 89.47% 90.00% 89.47% 89.47% 86.84% 86.36%52.63% 52.17% — — ha1p_39189 84.21% 83.33% 84.21% 84.21% 84.21% 86.36%81.58% 78.26% — — ha1p_39752 65.79% 65.00% 68.42% 68.42% 57.89% 59.09%86.84% 86.96% — — ha1p_62183 84.21% 83.33% 81.58% 81.58% 86.84% 86.36%94.74% 95.65% — — ha1p_69418 86.84% 86.67% 86.84% 86.84% 89.47% 90.91%100.00% 100.00% 70.00% 75.00% ha1p_71224 76.32% 76.67% 76.32% 76.32%76.32% 77.27% 78.95% 78.26% — — ha1p_76289 73.68% 73.33% 73.68% 73.68%73.68% 72.73% 76.32% 78.26% — — ha1p_81050 89.47% 90.00% 84.21% 84.21%94.74% 95.45% 94.74% 95.65% 70.00% 75.00% ha1p_81674 65.79% 68.33%71.05% 68.42% 63.16% 63.64% 63.16% 65.22% 50.00% 50.00% ha1p_8635557.89% 56.67% 55.26% 55.26% 60.53% 59.09% 84.21% 82.61% — — ha1p_9849155.26% 55.00% 55.26% 55.26% 52.63% 54.55% 92.11% 91.30% 60.00% 62.50%ha1p_99426 86.84% 88.33% 86.84% 86.84% 86.84% 86.36% 94.74% 95.65%100.00%  100.00%  ha1p16_00182 78.95% 78.33% 76.32% 76.32% 81.58% 81.82%89.47% 91.30% 90.00% 87.50% ha1p16_00185 76.32% 76.67% 76.32% 76.32%76.32% 77.27% 84.21% 86.96% 100.00%  100.00%  ha1p16_00193 78.95% 78.33%73.68% 73.68% 84.21% 86.36% 86.84% 86.96% 80.00% 75.00% ha1p16_0025978.95% 78.33% 78.95% 78.95% 78.95% 81.82% 78.95% 82.61% — — TH2B 76.32%76.67% 73.68% 73.68% 78.95% 77.27% 86.84% 100.00% — —

Example 7 Determination of Sensitivity and Specificity by ReceiverOperating Characteristics (ROC) Analysis

Receiver Operating Characteristic (ROC) analysis (see Lasko et al,Journal of Biomedical Informatics 38(5):404-415 (2005)) was used todetermine empirical cut-off values for classification of tissue samples.The analysis was performed independently for each of the forty-two loci,as well as for each of the following comparisons: Tumor vs. Normal(non-diseased ovary) and Tumor vs. Blood. In Table 6, the calculatedsensitivity and specificity are reported for both of the paradigms. Ineach case, sensitivity is reported as the true positive rate and1-specificity is reported as the false positive rate. For each depictedlocus, sensitivity refers to the percentage of tumor samples that reporta value above (for a gain of DNA methylation event in tumor) or below(for a loss of DNA methylation event in tumor) a threshold valuedetermined by ROC analysis. Specificity refers to the percentage ofnormal samples that report a value below (for a gain of DNA methylationevent in tumor) or above (for a loss of DNA methylation event in tumor)a threshold value determined by ROC analysis.

TABLE 6 Sensitivity and Specificity of Differentially Methylated Loci asDetermined by ROC Analysis in a Panel of Tumor Ovary vs. Normal Ovary(Normal), Tumor Ovary vs. Normal Blood (Blood) and Tumor Cervix vs.Normal Cervix (Cervical) Feature Normal Blood Cervical Feature Name SeqSensitivity Specificity Sensitivity Specificity Sensitivity SpecificityCHR01P001976799 1 96.15% 96.30% 73.08% 86.96% — — CHR01P026794862 246.15% 44.44% 69.23% 69.57% — — CHR01P043164342 3 92.31% 100.00% 88.46%86.96% 80.00% 75.00% CHR01P063154999 4 96.15% 96.30% 100.00% 100.00%90.00% 87.50% CHR01P204123050 5 65.38% 66.67% 65.38% 65.22% — —CHR01P206905110 6 92.31% 96.30% 88.46% 86.96% 80.00% 75.00%CHR01P225608458 7 80.77% 81.48% 50.00% 47.83% — — CHR02P005061785 896.15% 96.30% 80.77% 86.96% — — CHR02P042255672 9 92.31% 92.59% 73.08%73.91% — — CHR02P223364582 10 88.46% 88.89% 92.31% 91.30% 70.00% 75.00%CHR03P027740753 11 88.46% 88.89% 88.46% 86.96% 100.00%  100.00% CHR03P052525960 12 76.92% 77.78% 84.62% 82.61% 60.00% 62.50%CHR03P069745999 13 80.77% 81.48% 69.23% 69.57% — — CHR05P059799713 1469.23% 70.37% 92.31% 91.30% 60.00% 62.50% CHR05P059799813 15 73.08%74.07% 96.15% 95.65% 80.00% 75.00% CHR05P177842690 16 84.62% 85.19%80.77% 78.26% — — CHR06P010694062 17 88.46% 88.89% 69.23% 69.57% — —CHR06P026333318 18 96.15% 96.30% 73.08% 73.91% — — CHR08P102460854 1988.46% 88.89% 76.92% 100.00% — — CHR08P102461254 20 92.31% 92.59% 88.46%100.00% 80.00% 75.00% CHR08P102461554 21 88.46% 88.89% 92.31% 100.00%80.00% 75.00% CHR09P000107988 22 84.62% 85.19% 84.62% 82.61% 90.00%87.50% CHR09P021958839 23 88.46% 88.89% 92.31% 91.30% 90.00% 87.50%CHR09P131048752 24 96.15% 96.30% 73.08% 73.91% — — CHR10P118975684 2576.92% 77.78% 88.46% 86.96% 100.00%  100.00%  CHR11P021861414 26 65.38%66.67% 61.54% 60.87% — — CHR12P004359362 27 80.77% 81.48% 65.38% 65.22%— — CHR12P016001231 28 73.08% 74.07% 65.38% 65.22% — — CHR14P01889334429 92.31% 92.59% 88.46% 86.96% 100.00%  100.00%  CHR14P093230340 3092.31% 92.59% 100.00% 100.00% 90.00% 87.50% CHR16P066389027 32 73.08%74.07% 80.77% 91.30% 60.00% 62.50% CHR16P083319654 33 76.92% 77.78%69.23% 69.57% — — CHR18P019705147 34 96.15% 96.30% 80.77% 82.61% 90.00%87.50% CHR19P018622408 35 92.31% 92.59% 84.62% 82.61% 100.00%  100.00% CHRXP013196410 37 88.46% 88.89% 69.23% 69.57% — — CHRXP013196870 3888.46% 88.89% 76.92% 78.26% — — ha1p16_00179_l50 39 88.46% 88.89% 92.31%91.30% 90.00% 87.50% ha1p16_00182_l50 40 92.31% 92.59% 96.15% 95.65%80.00% 75.00% ha1p_12601_l50 42 92.31% 92.59% 69.23% 69.57% — —ha1p_17147_l50 43 92.31% 92.59% 50.00% 47.83% — — ha1p_42350_l50 4461.54% 62.96% 96.15% 95.65% 70.00% 75.00% ha1p_44897_l50 45 92.31%88.89% 53.85% 52.17% — — CHR01P001005050 47 100.00% 100.00% 100.00%100.00% 80.00% 100.00%  CHR16P001157479 48 92.86% 100.00% 100.00%100.00% 71.43% 100.00%

Example 8 Discriminatory Analysis to Determine which Locus orCombination of Loci Best Differentiate Between Cancerous andNon-Cancerous Tissue

To determine which locus or combination of loci best differentiatebetween cancerous (tumor) and non-cancerous (adjacent normal/benigndisease) tissue, discriminant analysis (Fischer, R. A. “The StatisticalUtilization of Multiple Measurements.” Annals of Eugenics, 8 (1938),376-386.; Lachenbruch, P. A. Discriminant Analysis. New York: HafnerPress, 1975) was utilized. A training dataset consisted of delta Ctvalues for forty-two loci across a panel of ten tumor samples and tennormal samples. Discriminant analysis on the training set identified twocombinations of four loci each that were able to correctly classify alltwenty samples (i.e., error rate=0%) as shown in Tables 7 and 8. Themodels developed on the training set were validated on a test dataset oftwenty-six tumor samples and twenty-seven normal samples. Error rates of0% and 1.92% were achieved when classifying tumor vs. normal using eachof the two models (see Table 9 and 10).

TABLE 7 Discriminant analysis results from training data, Model 1:CHR01P001976799, CHR14P093230340, ha1p_42350_l50, ha1p_44897_l50.Overall error rate = 0%. Predicted Group Normal Tumor Total Known GroupNormal 27  0 27 100%  0% Tumor  0 26 26  0% 100% Total 27 26

TABLE 8 Discriminant analysis results from training data, Model 2:CHR14P093230340, ha1p_12601_l50, ha1p_42350_l50, ha1p_44897_l50. Overallerror rate = 0%. Predicted Group Normal Tumor Total Known Group Normal27  0 27 100%  0% Tumor  0 26 26  0% 100% Total 27 26

TABLE 9 Discriminant analysis results from Model 1 (CHR01P001976799,CHR14P093230340, ha1p_42350_l50, ha1p_44897_l50) on test data. Overallerror rate = 0%. Predicted Group Normal Tumor Total Known Group Normal27  0 27 100%  0% Tumor  0 26 26  0% 100% Total 27 26

TABLE 10 Discriminant analysis results from Model 2 (CHR14P093230340,ha1p_12601_l50, ha1p_42350_l50, ha1p_44897_l50) on test data. Overallerror rate = 1.92%. Predicted Group Normal Tumor Total Known GroupNormal 27  0 27  100%   0% Tumor  1 25 26 3.85% 47.17% Total 27 26

Example 9 Selection of Sequence Identified as Potential Region ofDifferential DNA Methylation

As described in the examples above, the loci identified asdifferentially methylated were originally discovered based on DNAmethylation-dependent microarray analyses. The sequences of themicroarray features reporting this differential methylation areindicated in Table 2 and in section “SEQUENCE LISTING.” The “wingspan”of genomic interrogation by each array feature is proportional to thesize of the sheared target at the beginning of the experiment (e.g., 1to 4 Kbp), therefore regions of the genome comprising the probeparticipated the interrogation for differential methylation. Because theDNA was randomly sheared the effective genomic region scanned is roughlytwice the size of the average molecular weight. The smallest fragmentsin the molecular population were 1 Kb, this suggests the minimum regionsize. The largest fragments were 4 Kb in size, suggesting that eachprobe cannot monitor DNA methylation that is more than 4 Kbp proximal ordistal to each probe. PCR primers that amplify an amplicon within a 1 kbregion surrounding the sequence represented by each microarray featurewere selected and used for independent verification and validationexperiments. Primer sequences and amplicon sequences are indicated inTable 2 and in section “SEQUENCE LISTING.” To optimize successful PCRamplification, these amplicons were designed to be less than the entire1 kb region represented by the wingspan of the microarray feature.However, it should be noted that differential methylation may bedetectable anywhere within this 1-8 Kb sequence window adjacent to theprobe.

In addition, the local CG density surrounding each region wascalculated. Approximately 10 kb of sequence both upstream and downstreamof each feature was extracted from the human genome. For each 20 kbportion of the genome, a sliding window of 500 bp moving in 100 bp stepswas used to calculate the CG density. CG density was expressed as theratio of CG dinucleotides per kb. In this example, it is obvious that aregion anywhere within the ˜4 kb peak of CG density associated with thepromoter region of the gene could be monitored for DNA methylation andcould be important in development of a clinical diagnostic assay. As anobvious consequence, the more CG rich the DNA is adjacent to the probe,the more likely it is that the sequence would function redundantly toits neighboring sequences. Because of the technology platform's abilityto monitor this adjacent DNA for methylation differences, the sequencesindicated in Table 2 (DNA Region sequences) and in section “SEQUENCELISTING” were selected using an 8 Kb criteria.

Example 10 Applicability of DNA Methylation-Based Biomarkers inAdditional Tumor Types

To address the applicability of the differential DNA methylation eventsas biomarkers for additional tumor types, a subset of claimed loci wereanalyzed in a panel of 10 cervical tumor samples and 8 benign cervicalsamples. All tumors were squamous cell cervical carcinomas withhistology-confirmed neoplastic cellularity ranging from 75% to 95%.Benign cervical samples were obtained from hysterectomies of cervicalcancer-free women. Loci listed in Table 5 (columns labeled “cervical”)were analyzed using qPCR based assays. Receiver-operator characteristicanalysis (Lasko, et al. (2005) Journal of Biomedical Informatics38(5):404-415) was used to determine empirical threshold values forclassification of tissue samples. The analysis was performedindependently for each locus. Resulting sensitivity [the percentage oftumor samples above (gain biomarkers) or below (loss biomarkers)threshold] and specificity [the percentage of benign samples below (gainbiomarkers) or above (loss biomarkers) threshold] calculations are shownin Table 5. These results demonstrate that loci discovered to bedifferentially methylated in tumors relative to normal or benign tissueare also relevant biomarkers of cancers.

Example 11 Bisulfite Sequencing Confirmation of Differential DNAMethylation of Additional Loci

Confirmation of differential DNA methylation was performed by bisulfitesequencing. Primers were designed to amplify a 400 bp amplicon withinthe 500 bp region of locus ha1p16_(—)00182_(—)150 analyzed by qPCR (asdiscussed in the examples above) from bisulfite converted genomic DNA.Primers sequences lack CpG dinucleotides, and therefore amplifybisulfite converted DNA independently of DNA methylation status.Products were amplified from one tumor sample (positive for DNAmethylation) and from one normal sample. Amplicons were purified andcloned using TA cloning kits (Invitrogen). Ninety-six (96) independentclones were sequenced for the tumor sample. Ninety-six (96) independentclones were sequenced for the normal sample. Bisulfite treatment resultsin conversion of unmethylated cytosines to uracil, but does not convertmethylated cytosines. The percent methylation of each CpG dinucleotidewithin the region was calculated as the number of sequence reads of C ateach CpG divided by the total number of sequence reads. All 9 CpGdinucleotides are methylated in the tumor (occupancy ranging from 93.62to 100%). However, methylation occupancy of CpG dinucleotides in normalsample was lower, ranging from 0 to 10%.

To provide further confirmation of DNA methylation differences and tojustify the qPCR based strategy for high-throughput detection of DNAmethylation, three additional loci CHR01P043164342, CHR01P063154999,CHR03P027740753, were analyzed by bisulfite genomic sequencing asdescribed above. Ninety six independent clones were sequenced peramplicon per sample. The sequencing results were consistent with theresults of qPCR. Note that CHR01P043164342 is a DNA methylation lossmarker, and this sequence is less methylated in tumor sample relative tothe normal sample. In addition, two other loci were analyzed bybisulfite genomic sequencing as described above. Between 10 and 24independent clones were sequenced per amplicon per sample. Thesequencing results were in line with the qPCR results (see Table 11).Note that the CG Position column in Table 11 refers to the CG positionin the amplicons used for bisulfite sequencing.

TABLE 11 Examples of Bisulfite Analysis of Differentially MethylatedLoci. CG % of Methylation Feature Name Position Tumor Benign ha1p_0367127   85%   18% 58   90%   27% 70   90%   18% 79   85%    9% 89   80%   9% 96   85%    9% 115   85%    9% 125   83%    9% 134   83%    0% #of clones 20 11 qPCR result (delta Ct)   2.7    0.91 CG % Methylationposition Tumor Adj. Normal ha1p_08588 35   46%   90% 67   38%   82% 182  21%   50% 200    7%   40% # of clones 24 11 qPCR result (delta Ct)   0.95    6.23 CG % Methylation position Tumor Normal CHR01P04316434239  9.91% 90.52% 51  7.21% 75.68% 60  6.19% 70.99% 87 10.00% 92.86% 104 9.46% 83.81% 139  3.77% 32.56% 148 13.46% 86.42% 174  9.80% 61.54% 183 7.69% 48.68% 255  6.38% 67.27% # of clones 96 96 qPCR result (delta Ct)   0.025    7.46 CHR01P063154999 32 76.40% 15.05% 34 96.63%  7.53% 5596.63% 23.60% 66 92.13%  4.44% 73 97.70%  4.40% 89 94.32%  2.20% 9192.13%  3.30% 94 93.18%  0.00% 100 92.13%  1.10% 110 97.73%  1.14% 11896.59%  2.22% 128 97.73%  2.25% # of clones 96 96 qPCR result (delta Ct)  4.14  0 CHR03P027740753 26 93.14% 11.76% 28 96.04%  17.6% 93 29.21% 6.67% 136 52.24%  0.00% 157 91.04%  0.00% 159 92.42%  0.00% 171 98.48%13.33% 180 81.54% 14.29% # of clones 96 96 qPCR result (delta Ct)   4.675  0

Example 12 Analysis of DNA Methylation in Various Cancer Types

To address the applicability of the claimed DNA methylation biomarkersto cancer types other than one type of cancer, all claimed biomarkerswere analyzed in panels of bladder, breast, cervical, colon,endometrial, esophageal, head and neck, liver, lung, melanoma, ovarian,prostate, renal, and thyroid tumors. Adjacent histology normal tissueswere analyzed as controls. In addition, melanoma tumors were analyzed,although no adjacent normal tissues were available. The number ofsamples analyzed for each cancer type is provided in Table 12. DNAmethylation was measured as described in these Examples. For each locusand each cancer type, the sensitivity and specificity for discriminatingbetween tumor and adjacent normal tissues are reported in Tables 13-27.For melanoma tumors (Table 23), only sensitivity (the frequency of DNAmethylation detection (ie. samples that report and average dCt≧1.0)) isreported due to the unavailability of adjacent normal tissues. For eachlocus, the optimal threshold for discriminating between tumor andadjacent normal tissue was calculated following ROC curve analysis.These data demonstrate that particular biomarker loci are applicable tomore than just one cancer type.

TABLE 12 Number of tumor and normal samples tested for the biomarkerloci. Cancer Type Tumor Normal Bladder 9 9 Breast 10 10 Cervical 10 9Colon 10 10 Endometrial 14 9 Esophageal 9 10 Head & Neck 9 5 Liver 9 9Lung 20 20 Melanoma 7 0 Ovarian 34 35 Prostate 9 9 Renal 10 10 Thyroid10 10

TABLE 13 Sensitivity and Specificity of differentially methylated lociin bladder tumors relative to adjacent histological normal bladdertissue. Pos. Neg. Feature Name Locus Number Threshold Sensitivity ofTotal Specificity of Total CHR01P001976799 1 6 77.78% 7 of 9 88.89% 8 of9 CHR01P026794862 2 0.52 62.50% 5 of 8 50.00% 3 of 6 CHR01P043164342 34.69 66.67% 6 of 9 77.78% 7 of 9 CHR01P063154999 4 0.955 100.00%  9 of 966.67% 6 of 9 CHR01P204123050 5 1.265 50.00% 4 of 8 100.00%  7 of 7CHR01P206905110 6 2.685 88.89% 8 of 9 88.89% 8 of 9 CHR01P225608458 71.655 100.00%  9 of 9 88.89% 8 of 9 CHR02P005061785 8 4.945 66.67% 6 of9 75.00% 6 of 8 CHR02P042255672 9 2.5 66.67% 6 of 9 77.78% 7 of 9CHR02P223364582 10 1.65 66.67% 6 of 9 100.00%  9 of 9 CHR03P027740753 111.225 100.00%  9 of 9 100.00%  8 of 8 CHR03P052525960 12 3.225 88.89% 8of 9 100.00%  9 of 9 CHR03P069745999 13 3.255 55.56% 5 of 9 66.67% 6 of9 CHR05P059799713 14 1.13 77.78% 7 of 9 55.56% 5 of 9 CHR05P059799813 150.715 100.00%  8 of 8 37.50% 3 of 8 CHR05P177842690 16 2.79 77.78% 7 of9 50.00% 4 of 8 CHR06P010694062 17 4.32 66.67% 6 of 9 100.00%  9 of 9CHR06P026333318 18 4.31 77.78% 7 of 9 100.00%  9 of 9 CHR08P102460854 190.805 77.78% 7 of 9 100.00%  9 of 9 CHR08P102461254 20 1.09 88.89% 8 of9 100.00%  9 of 9 CHR08P102461554 21 0.97 77.78% 7 of 9 88.89% 8 of 9CHR09P000107988 22 1.65 88.89% 8 of 9 100.00%  9 of 9 CHR09P021958839 231.73 77.78% 7 of 9 88.89% 8 of 9 CHR09P131048752 24 4.21 88.89% 8 of 9100.00%  9 of 9 CHR10P118975684 25 1.295 77.78% 7 of 9 66.67% 6 of 9CHR11P021861414 26 2.96 88.89% 8 of 9 88.89% 8 of 9 CHR12P004359362 272.25 66.67% 6 of 9 77.78% 7 of 9 CHR12P016001231 28 0.965 25.00% 2 of 8100.00%  8 of 8 CHR14P018893344 29 3.335 55.56% 5 of 9 100.00%  8 of 8CHR14P093230340 30 1.91 77.78% 7 of 9 85.71% 6 of 7 CHR16P000373719 311.305 100.00%  8 of 8 75.00% 3 of 4 CHR16P066389027 32 1.47 55.56% 5 of9 77.78% 7 of 9 CHR16P083319654 33 1.575 66.67% 6 of 9 100.00%  9 of 9CHR18P019705147 34 3.85 100.00%  9 of 9 55.56% 5 of 9 CHR19P018622408 352.795 100.00%  9 of 9 87.50% 7 of 8 CHR19P051892823 36 2.095 80.00% 4 of5 100.00%  4 of 4 CHRXP013196410 37 2.63 66.67% 6 of 9 88.89% 8 of 9CHRXP013196870 38 2.255 55.56% 5 of 9 55.56% 5 of 9 ha1p16_00179_l50 391.44 88.89% 8 of 9 100.00%  9 of 9 ha1p16_00182_l50 40 1.45 77.78% 7 of9 100.00%  9 of 9 ha1p16_00257_l50 41 1.42 66.67% 6 of 9 87.50% 7 of 8ha1p_12601_l50 42 1.245 88.89% 8 of 9 100.00%  9 of 9 ha1p_17147_l50 431.12 75.00% 6 of 8 88.89% 8 of 9 ha1p_42350_l50 44 5.11 62.50% 5 of 850.00% 4 of 8 ha1p_44897_l50 45 1.645 100.00%  9 of 9 85.71% 6 of 7ha1p_61253_l50 46 2.61 75.00% 6 of 8 100.00%  7 of 7 CHR01P001005050 471.745 75.00% 6 of 8 100.00%  7 of 7 CHR16P001157479 48 — — — — —ha1g_00681 49 1.68   44% 4 of 9   100% 9 of 9 ha1g_01966 50 1.99   89% 8of 9   100% 9 of 9 ha1g_02153 51 1.51   56% 5 of 9   100% 9 of 9ha1g_02319 52 0.64   100% 9 of 9   89% 8 of 9 ha1g_02335 53 4.24   78% 7of 9   33% 3 of 9 ha1p16_00182 54 0.73   100% 9 of 9   67% 6 of 9ha1p16_00185 55 1.12   67% 6 of 9   100% 9 of 9 ha1p16_00193 56 1.94  56% 5 of 9   100% 9 of 9 ha1p16_00259 57 2.17   88% 7 of 8   78% 7 of9 ha1p_02799 58 2.35   56% 5 of 9   100% 9 of 9 ha1p_03567 59 1.06   67%6 of 9   75% 6 of 8 ha1p_03671 60 1.15   89% 8 of 9   89% 8 of 9ha1p_05803 61 2.09   67% 6 of 9   100% 9 of 9 ha1p_07131 62 3.52   78% 7of 9   89% 8 of 9 ha1p_07989 63 2.06   78% 7 of 9   88% 7 of 8ha1p_08588 64 3.96   67% 6 of 9   100% 9 of 9 ha1p_09700 65 0.77   75% 6of 8   100% 8 of 8 ha1p_104458 66 3.43   56% 5 of 9   100% 9 of 9ha1p_105287 67 2.96   100% 9 of 9   89% 8 of 9 ha1p_10702 68 3.06   67%6 of 9   100% 8 of 8 ha1p_108469 69 1.54   33% 3 of 9   100% 9 of 9ha1p_108849 70 3.42   67% 6 of 9   100% 9 of 9 ha1p_11016 71 2.92   100%9 of 9   100% 9 of 9 ha1p_11023 72 2.91   56% 5 of 9   100% 9 of 9ha1p_12974 73 0.53   56% 5 of 9   78% 7 of 9 ha1p_16027 74 2.2   44% 4of 9   89% 8 of 9 ha1p_16066 75 2.25   56% 5 of 9   78% 7 of 9ha1p_18911 76 2.77   44% 4 of 9   100% 8 of 8 ha1p_19254 77 1.95   78% 7of 9   100% 9 of 9 ha1p_19853 78 0.79   78% 7 of 9   89% 8 of 9ha1p_22257 79 2.7   67% 6 of 9   100% 9 of 9 ha1p_22519 80 1.41   89% 8of 9   78% 7 of 9 ha1p_31800 81 2.65   50% 3 of 6   89% 8 of 9ha1p_33290 82 2.62   89% 8 of 9   100% 9 of 9 ha1p_37635 83 6   100% 9of 9    0% 0 of 9 ha1p_39189 84 0.78   100% 9 of 9   78% 7 of 9ha1p_39511 85 3.02   44% 4 of 9   100% 9 of 9 ha1p_39752 86 2.51   56% 5of 9   100% 9 of 9 ha1p_60945 87 2   67% 6 of 9   100% 9 of 9 ha1p_6218388 4.11   22% 2 of 9   100% 9 of 9 ha1p_69418 89 2.64   78% 7 of 9  100% 8 of 8 ha1p_71224 90 1.83   89% 8 of 9   100% 9 of 9 ha1p_7422191 1.98   56% 5 of 9   89% 8 of 9 ha1p_76289 92 1.11   78% 7 of 9   100%9 of 9 ha1p_81050 93 3.95   78% 7 of 9   89% 8 of 9 ha1p_81674 94 1.82  67% 6 of 9   100% 9 of 9 ha1p_86355 95 1.18   89% 8 of 9   75% 6 of 8ha1p_98491 96 3.99   33% 3 of 9   100% 9 of 9 ha1p_99426 97 1.34   67% 6of 9   100% 9 of 9 Threshold: Average dCt value established by ROC curveanalysis as optimal threshold for distinguishing tumor and adjacentnormal tissues. Sensitivity: % of positive (i.e., methylation scoreabove Threshold for gain of methylation markers or below Threshold forloss of methylation markers) tumors. Pos. of Total: Number of positivetumors relative to the total number of tumors analyzed. Specificity: %of negative (i.e., methylation score below Threshold for gain ofmethylation markers or above Threshold for loss of methylation markers)adjacent normal samples. Neg. of Total: Number of negative adjacentnormal samples relative to the total number of adjacent normal samplesanalyzed.

TABLE 14 Sensitivity and Specificity of differentially methylated lociin breast tumors relative to adjacent histological normal breast tissue.Locus Pos. Neg. Feature Name Number Threshold Sensitivity of TotalSpecificity of Total CHR01P001976799 1 5.18 70.00% 7 of 10 100.00%  10of 10  CHR01P026794862 2 2.075 100.00%  8 of 8  33.33% 1 of 3 CHR01P043164342 3 1.8 50.00% 5 of 10 80.00% 8 of 10 CHR01P063154999 42.295 60.00% 6 of 10 90.00% 9 of 10 CHR01P204123050 5 1.655 80.00% 8 of10 60.00% 6 of 10 CHR01P206905110 6 2.165 70.00% 7 of 10 90.00% 9 of 10CHR01P225608458 7 1.9 80.00% 8 of 10 90.00% 9 of 10 CHR02P005061785 82.18 80.00% 8 of 10 90.00% 9 of 10 CHR02P042255672 9 5.895 70.00% 7 of10 70.00% 7 of 10 CHR02P223364582 10 2.625 60.00% 6 of 10 70.00% 7 of 10CHR03P027740753 11 1.62 70.00% 7 of 10 100.00%  10 of 10 CHR03P052525960 12 2.4 50.00% 5 of 10 100.00%  10 of 10  CHR03P06974599913 0.775 40.00% 4 of 10 100.00%  10 of 10  CHR05P059799713 14 1.4310.00% 1 of 10 100.00%  9 of 9  CHR05P059799813 15 0.765 66.67% 6 of 9 55.56% 5 of 9  CHR05P177842690 16 1.29 70.00% 7 of 10 80.00% 8 of 10CHR06P010694062 17 3.46 70.00% 7 of 10 90.00% 9 of 10 CHR06P026333318 181.155 50.00% 5 of 10 100.00%  10 of 10  CHR08P102460854 19 0.615100.00%  10 of 10  40.00% 4 of 10 CHR08P102461254 20 0.525 100.00%  10of 10  50.00% 5 of 10 CHR08P102461554 21 0.695 100.00%  10 of 10  40.00%4 of 10 CHR09P000107988 22 1.97 40.00% 4 of 10 80.00% 8 of 10CHR09P021958839 23 2.805 20.00% 2 of 10 100.00%  10 of 10 CHR09P131048752 24 2.22 90.00% 9 of 10 90.00% 9 of 10 CHR10P118975684 252.695 40.00% 4 of 10 100.00%  10 of 10  CHR11P021861414 26 3.98 70.00% 7of 10 100.00%  10 of 10  CHR12P004359362 27 1.91 50.00% 5 of 10 80.00% 8of 10 CHR12P016001231 28 1.515 70.00% 7 of 10 62.50% 5 of 8 CHR14P018893344 29 2.585 80.00% 8 of 10 100.00%  10 of 10 CHR14P093230340 30 3.66 30.00% 3 of 10 100.00%  10 of 10 CHR16P000373719 31 1.16 33.33% 3 of 9  88.89% 8 of 9  CHR16P066389027 320.935 60.00% 6 of 10 90.00% 9 of 10 CHR16P083319654 33 1.635 80.00% 8 of10 90.00% 9 of 10 CHR18P019705147 34 3.435 50.00% 5 of 10 90.00% 9 of 10CHR19P018622408 35 2.595 80.00% 8 of 10 100.00%  10 of 10 CHR19P051892823 36 3.53 100.00%  4 of 4  100.00%  8 of 8  CHRXP01319641037 1.63 66.67% 6 of 9  88.89% 8 of 9  CHRXP013196870 38 1.71 60.00% 6 of10 100.00%  9 of 9  ha1p16_00179_l50 39 1.4 30.00% 3 of 10 100.00%  10of 10  ha1p16_00182_l50 40 0.99 30.00% 3 of 10 100.00%  10 of 10 ha1p16_00257_l50 41 2.5 30.00% 3 of 10 100.00%  10 of 10  ha1p_12601_l5042 0.99 100.00%  10 of 10  80.00% 8 of 10 ha1p_17147_l50 43 0.99100.00%  10 of 10  80.00% 8 of 10 ha1p_42350_l50 44 5.27 50.00% 5 of 1080.00% 8 of 10 ha1p_44897_l50 45 2.76 40.00% 4 of 10 90.00% 9 of 10ha1p_61253_l50 46 1.37 80.00% 8 of 10 90.00% 9 of 10 CHR01P001005050 470.605 70.00% 7 of 10 75.00% 6 of 8  CHR16P001157479 48 — — — — —ha1g_00681 49 1.66   90% 9 of 10   100% 10 of 10  ha1g_01966 50 3.37  60% 6 of 10   90% 9 of 10 ha1g_02153 51 2.72   50% 5 of 10   90% 9 of10 ha1g_02319 52 2.03   50% 5 of 10   100% 10 of 10  ha1g_02335 53 2.4  90% 9 of 10   90% 9 of 10 ha1p16_00182 54 1.55   60% 6 of 10   50% 5of 10 ha1p16_00185 55 1.65   60% 6 of 10   90% 9 of 10 ha1p16_00193 562.89   60% 6 of 10   80% 8 of 10 ha1p16_00259 57 5.08   20% 2 of 10  100% 10 of 10  ha1p_02799 58 4.22   80% 8 of 10   60% 6 of 10ha1p_03567 59 1.46   100% 10 of 10   50% 3 of 6  ha1p_03671 60 0.59  40% 4 of 10   90% 9 of 10 ha1p_05803 61 2.97   67% 4 of 6   86% 6 of7  ha1p_07131 62 5.55   100% 7 of 7   86% 6 of 7  ha1p_07989 63 2.18  100% 9 of 9   88% 7 of 8  ha1p_08588 64 5.9   100% 10 of 10   90% 9 of10 ha1p_09700 65 0.72   44% 4 of 9   89% 8 of 9  ha1p_104458 66 6   20%2 of 10   90% 9 of 10 ha1p_105287 67 0.82   40% 4 of 10   80% 8 of 10ha1p_10702 68 1.57   60% 6 of 10   100% 9 of 9 ha1p_108469 69 1.77   78%7 of 9   90% 9 of 10 ha1p_108849 70 1.78   80% 8 of 10   80% 8 of 10ha1p_11016 71 3.69   90% 9 of 10   90% 9 of 10 ha1p_11023 72 3.02   80%8 of 10   80% 8 of 10 ha1p_12974 73 0.94   60% 6 of 10   63% 5 of 8 ha1p_16027 74 1.81   80% 8 of 10   70% 7 of 10 ha1p_16066 75 2.33   70%7 of 10   90% 9 of 10 ha1p_18911 76 3.31   100% 9 of 9    30% 3 of 10ha1p_19254 77 3.66   100% 10 of 10    90% 9 of 10 ha1p_19853 78 1.18  80% 8 of 10   80% 8 of 10 ha1p_22257 79 3.55   50% 5 of 10   100% 10of 10  ha1p_22519 80 1.64   100% 9 of 9    90% 9 of 10 ha1p_31800 813.68   60% 6 of 10   100% 10 of 10  ha1p_33290 82 2.26   90% 9 of 10  100% 10 of 10  ha1p_37635 83 5.41   100% 10 of 10     0% 0 of 10ha1p_39189 84 1.12   88% 7 of 8    89% 8 of 9  ha1p_39511 85 3.3   60% 6of 10   78% 7 of 9  ha1p_39752 86 3.99   30% 3 of 10   100% 10 of 10 ha1p_60945 87 1.84   70% 7 of 10   50% 5 of 10 ha1p_62183 88 4.05   90%9 of 10   90% 9 of 10 ha1p_69418 89 2.39   60% 6 of 10   90% 9 of 10ha1p_71224 90 2.01   50% 5 of 10   90% 9 of 10 ha1p_74221 91 1.12   80%8 of 10   50% 5 of 10 ha1p_76289 92 1.12   89% 8 of 9    86% 6 of 7 ha1p_81050 93 4.34   100% 10 of 10    90% 9 of 10 ha1p_81674 94 1.9  90% 9 of 10   100% 8 of 8  ha1p_86355 95 2.08   70% 7 of 10   100% 8of 8  ha1p_98491 96 4.17   70% 7 of 10   80% 8 of 10 ha1p_99426 97 1.2  90% 9 of 10   90% 9 of 10 Threshold: Average dCt value established byROC curve analysis as optimal threshold for distinguishing tumor andadjacent normal tissues. Sensitivity: % of positive (i.e., methylationscore above Threshold for gain of methylation markers or below Thresholdfor loss of methylation markers) tumors. Pos. of Total: Number ofpositive tumors relative to the total number of tumors analyzed.Specificity: % of negative (i.e., methylation score below Threshold forgain of methylation markers or above Threshold for loss of methylationmarkers) adjacent normal samples. Neg. of Total: Number of negativeadjacent normal samples relative to the total number of adjacent normalsamples analyzed.

TABLE 15 Sensitivity and Specificity of differentially methylated lociin cervical tumors relative to adjacent histological normal cervicaltissue. Locus Pos. of Neg. of Feature Name Number Threshold SensitivityTotal Specificity Total CHR01P001976799 1 2.985 100.00%  10 of 10 100.00%  9 of 9 CHR01P026794862 2 0.985 50.00% 3 of 6  87.50% 7 of 8CHR01P043164342 3 2.535 60.00% 6 of 10 100.00%  8 of 8 CHR01P063154999 41.165 90.00% 9 of 10 88.89% 8 of 9 CHR01P204123050 5 1.87 60.00% 6 of 1088.89% 8 of 9 CHR01P206905110 6 2.83 80.00% 8 of 10 100.00%  9 of 9CHR01P225608458 7 2 80.00% 8 of 10 100.00%  9 of 9 CHR02P005061785 81.99 100.00%  10 of 10  100.00%  9 of 9 CHR02P042255672 9 2.02 80.00% 8of 10 100.00%  9 of 9 CHR02P223364582 10 2.11 70.00% 7 of 10 77.78% 7 of9 CHR03P027740753 11 0.96 90.00% 9 of 10 100.00%  9 of 9 CHR03P05252596012 1.74 70.00% 7 of 10 100.00%  9 of 9 CHR03P069745999 13 2.955 60.00% 6of 10 100.00%  9 of 9 CHR05P059799713 14 0.89 50.00% 5 of 10 88.89% 8 of9 CHR05P059799813 15 0.735 30.00% 3 of 10 88.89% 8 of 9 CHR05P17784269016 1.885 40.00% 4 of 10 100.00%  9 of 9 CHR06P010694062 17 2.38 90.00% 9of 10 100.00%  9 of 9 CHR06P026333318 18 1.97 90.00% 9 of 10 100.00%  7of 7 CHR08P102460854 19 0.925 90.00% 9 of 10 75.00% 6 of 8CHR08P102461254 20 1.305 80.00% 8 of 10 100.00%  9 of 9 CHR08P10246155421 1.1 80.00% 8 of 10 100.00%  9 of 9 CHR09P000107988 22 1.6 70.00% 7 of10 100.00%  9 of 9 CHR09P021958839 23 1.27 70.00% 7 of 10 77.78% 7 of 9CHR09P131048752 24 3.68 70.00% 7 of 10 66.67% 6 of 9 CHR10P118975684 251.28 80.00% 8 of 10 66.67% 6 of 9 CHR11P021861414 26 5.505 80.00% 8 of10 88.89% 8 of 9 CHR12P004359362 27 3.645 30.00% 3 of 10 100.00%  9 of 9CHR12P016001231 28 1.56 100.00%  10 of 10  77.78% 7 of 9 CHR14P01889334429 1.255 100.00%  10 of 10  100.00%  9 of 9 CHR14P093230340 30 1.69588.89% 8 of 9  85.71% 6 of 7 CHR16P000373719 31 2.135 87.50% 7 of 8 80.00% 4 of 5 CHR16P066389027 32 1.22 60.00% 6 of 10 55.56% 5 of 9CHR16P083319654 33 2.885 100.00%  10 of 10  66.67% 6 of 9CHR18P019705147 34 2.815 80.00% 8 of 10 100.00%  9 of 9 CHR19P01862240835 1.525 100.00%  10 of 10  100.00%  9 of 9 CHR19P051892823 36 1.44566.67% 4 of 6  100.00%  4 of 4 CHRXP013196410 37 1.545 80.00% 8 of 10100.00%  9 of 9 CHRXP013196870 38 1.48 80.00% 8 of 10 85.71% 6 of 7ha1p16_00179_l50 39 1.555 66.67% 6 of 9  87.50% 7 of 8 ha1p16_00182_l5040 1.205 70.00% 7 of 10 100.00%  9 of 9 ha1p16_00257_l50 41 0.705 70.00%7 of 10 77.78% 7 of 9 ha1p_12601_l50 42 2.245 100.00%  10 of 10 100.00%  9 of 9 ha1p_17147_l50 43 2.455 90.00% 9 of 10 100.00%  9 of 9ha1p_42350_l50 44 2.335 66.67% 6 of 9  77.78% 7 of 9 ha1p_44897_l50 453.755 80.00% 8 of 10 85.71% 6 of 7 ha1p_61253_l50 46 1.34 100.00%  10 of10  100.00%  6 of 6 CHR01P001005050 47 2.025 80.00% 8 of 10 100.00%  9of 9 CHR16P001157479 48 4.045 71.43% 5 of 7  100.00%  2 of 2 ha1g_0068149 0.91   80% 8 of 10   100% 9 of 9 ha1g_01966 50 1.71   100% 10 of 10   67% 6 of 9 ha1g_02153 51 1.41   100% 10 of 10    100% 9 of 9ha1g_02319 52 0.99   100% 10 of 10    89% 8 of 9 ha1g_02335 53 4.96  60% 6 of 10   67% 6 of 9 ha1p16_00182 54 1.13   80% 8 of 10   89% 8 of9 ha1p16_00185 55 0.96   80% 8 of 10   100% 9 of 9 ha1p16_00193 56 1.74  80% 8 of 10   89% 8 of 9 ha1p16_00259 57 1.84   90% 9 of 10   89% 8 of9 ha1p_02799 58 3.12   20% 2 of 10   100% 9 of 9 ha1p_03567 59 1.89  100% 10 of 10    78% 7 of 9 ha1p_03671 60 1.14   80% 8 of 10   100% 9of 9 ha1p_05803 61 1.3   100% 10 of 10    89% 8 of 9 ha1p_07131 62 5.67  70% 7 of 10   100% 9 of 9 ha1p_07989 63 4.72   89% 8 of 9    75% 6 of8 ha1p_08588 64 6   70% 7 of 10   89% 8 of 9 ha1p_09700 65 0.59   70% 7of 10   89% 8 of 9 ha1p_104458 66 4.45   70% 7 of 10   78% 7 of 9ha1p_105287 67 2.56   70% 7 of 10   100% 9 of 9 ha1p_10702 68 2.09   60%6 of 10   100% 9 of 9 ha1p_108469 69 1.27   100% 10 of 10    44% 4 of 9ha1p_108849 70 3.01   100% 10 of 10    78% 7 of 9 ha1p_11016 71 3.18  100% 10 of 10    56% 5 of 9 ha1p_11023 72 2.52   90% 9 of 10   100% 9of 9 ha1p_12974 73 0.6   50% 5 of 10   89% 8 of 9 ha1p_16027 74 1.56  100% 10 of 10    56% 5 of 9 ha1p_16066 75 2.15   60% 6 of 10   88% 7of 8 ha1p_18911 76 3.08   60% 6 of 10   78% 7 of 9 ha1p_19254 77 4.53  90% 9 of 10   100% 8 of 8 ha1p_19853 78 0.55   100% 9 of 9    67% 6 of9 ha1p_22257 79 2.35   60% 6 of 10   78% 7 of 9 ha1p_22519 80 2.09   80%8 of 10   100% 9 of 9 ha1p_31800 81 2.88   90% 9 of 10   89% 8 of 9ha1p_33290 82 1.65   80% 8 of 10   100% 9 of 9 ha1p_37635 83 6   10% 1of 10   100% 9 of 9 ha1p_39189 84 0.86   100% 10 of 10    78% 7 of 9ha1p_39511 85 3.18   63% 5 of 8    89% 8 of 9 ha1p_39752 86 2.44   88% 7of 8    100% 8 of 8 ha1p_60945 87 1.44   80% 8 of 10   67% 6 of 9ha1p_62183 88 5.21   60% 6 of 10   56% 5 of 9 ha1p_69418 89 3.61   100%10 of 10    78% 7 of 9 ha1p_71224 90 1.99   80% 8 of 10   100% 9 of 9ha1p_74221 91 1.61   89% 8 of 9    100% 8 of 8 ha1p_76289 92 0.52   90%9 of 10   100% 8 of 8 ha1p_81050 93 6   50% 5 of 10   100% 9 of 9ha1p_81674 94 0.87   80% 8 of 10   78% 7 of 9 ha1p_86355 95 1.56   60% 6of 10   89% 8 of 9 ha1p_98491 96 3.3   70% 7 of 10   67% 6 of 9ha1p_99426 97 0.76   90% 9 of 10   100% 9 of 9 Threshold: Average dCtvalue established by ROC curve analysis as optimal threshold fordistinguishing tumor and adjacent normal tissues. Sensitivity: % ofpositive (i.e., methylation score above Threshold for gain ofmethylation markers or below Threshold for loss of methylation markers)tumors. Pos. of Total: Number of positive tumors relative to the totalnumber of tumors analyzed. Specificity: % of negative (i.e., methylationscore below Threshold for gain of methylation markers or above Thresholdfor loss of methylation markers) adjacent normal samples. Neg. of Total:Number of negative adjacent normal samples relative to the total numberof adjacent normal samples analyzed.

TABLE 16 Sensitivity and Specificity of differentially methylated lociin colon tumors relative to adjacent histological normal colon tissue.Locus Pos. of Neg. of Feature Name Number Threshold Sensitivity TotalSpecificity Total CHR01P001976799 1 5.86 80.00% 8 of 10 90.00% 9 of 10CHR01P026794862 2 0.88 50.00% 4 of 8  87.50% 7 of 8  CHR01P043164342 31.78 80.00% 8 of 10 60.00% 6 of 10 CHR01P063154999 4 1.14 80.00% 8 of 1090.00% 9 of 10 CHR01P204123050 5 1.655 40.00% 4 of 10 100.00%  10 of 10 CHR01P206905110 6 1.63 70.00% 7 of 10 80.00% 8 of 10 CHR01P225608458 71.97 80.00% 8 of 10 90.00% 9 of 10 CHR02P005061785 8 5.51 50.00% 5 of 1080.00% 8 of 10 CHR02P042255672 9 2.8 80.00% 8 of 10 100.00%  10 of 10 CHR02P223364582 10 2 80.00% 8 of 10 90.00% 9 of 10 CHR03P027740753 111.26 90.00% 9 of 10 80.00% 8 of 10 CHR03P052525960 12 2.825 100.00%  10of 10  80.00% 8 of 10 CHR03P069745999 13 3.6 70.00% 7 of 10 90.00% 9 of10 CHR05P059799713 14 1.025 50.00% 5 of 10 80.00% 8 of 10CHR05P059799813 15 0.89 40.00% 4 of 10 80.00% 8 of 10 CHR05P177842690 163.58 90.00% 9 of 10 20.00% 2 of 10 CHR06P010694062 17 3.055 90.00% 9 of10 90.00% 9 of 10 CHR06P026333318 18 3.175 90.00% 9 of 10 90.00% 9 of 10CHR08P102460854 19 0.825 90.00% 9 of 10 70.00% 7 of 10 CHR08P10246125420 0.69 80.00% 8 of 10 80.00% 8 of 10 CHR08P102461554 21 1.015 90.00% 9of 10 70.00% 7 of 10 CHR09P000107988 22 0.985 90.00% 9 of 10 80.00% 8 of10 CHR09P021958839 23 1.46 90.00% 9 of 10 100.00%  10 of 10 CHR09P131048752 24 3.695 70.00% 7 of 10 100.00%  10 of 10 CHR10P118975684 25 1.41 80.00% 8 of 10 90.00% 9 of 10 CHR11P021861414 263.79 90.00% 9 of 10 90.00% 9 of 10 CHR12P004359362 27 3.51 50.00% 5 of10 80.00% 8 of 10 CHR12P016001231 28 1.295 40.00% 4 of 10 90.00% 9 of 10CHR14P018893344 29 2.805 80.00% 8 of 10 90.00% 9 of 10 CHR14P09323034030 1.66 90.00% 9 of 10 70.00% 7 of 10 CHR16P000373719 31 1.525 40.00% 2of 5  87.50% 7 of 8  CHR16P066389027 32 0.655 90.00% 9 of 10 80.00% 8 of10 CHR16P083319654 33 1.79 90.00% 9 of 10 60.00% 6 of 10 CHR18P01970514734 2.01 80.00% 8 of 10 60.00% 6 of 10 CHR19P018622408 35 3.015 60.00% 6of 10 100.00%  10 of 10  CHR19P051892823 36 0.6 57.14% 4 of 7  100.00% 7 of 7  CHRXP013196410 37 2.99 60.00% 6 of 10 100.00%  10 of 10 CHRXP013196870 38 2.92 60.00% 6 of 10 100.00%  9 of 9  ha1p16_00179_l5039 1.425 80.00% 8 of 10 90.00% 9 of 10 ha1p16_00182_l50 40 1.22 80.00% 8of 10 90.00% 9 of 10 ha1p16_00257_l50 41 1.085 90.00% 9 of 10 90.00% 9of 10 ha1p_12601_l50 42 0.68 80.00% 8 of 10 80.00% 8 of 10ha1p_17147_l50 43 1.025 90.00% 9 of 10 60.00% 6 of 10 ha1p_42350_l50 443.865 80.00% 8 of 10 55.56% 5 of 9  ha1p_44897_l50 45 3.045 60.00% 6 of10 100.00%  10 of 10  ha1p_61253_l50 46 1.88 100.00%  10 of 10  90.00% 9of 10 CHR01P001005050 47 1.03 70.00% 7 of 10 66.67% 6 of 9 CHR16P001157479 48 2.055 50.00% 4 of 8  100.00%  9 of 9  ha1g_00681 491.61   70% 7 of 10   90% 9 of 10 ha1g_01966 50 1.76   100% 10 of 10   90% 9 of 10 ha1g_02153 51 1.51   80% 8 of 10   90% 9 of 10 ha1g_0231952 0.53   80% 8 of 10   90% 9 of 10 ha1g_02335 53 1.63   90% 9 of 10  80% 8 of 10 ha1p16_00182 54 1.17   90% 9 of 10   90% 9 of 10ha1p16_00185 55 1.08   90% 9 of 10   80% 8 of 10 ha1p16_00193 56 1.79  90% 9 of 10   80% 8 of 10 ha1p16_00259 57 2.89   90% 9 of 10   100% 10of 10  ha1p_02799 58 4.6   50% 5 of 10   100% 9 of 9  ha1p_03567 59 0.78  70% 7 of 10   30% 3 of 10 ha1p_03671 60 2.03   50% 5 of 10   89% 8 of9 ha1p_05803 61 2.22   80% 8 of 10   90% 9 of 10 ha1p_07131 62 4.26  100% 10 of 10    80% 8 of 10 ha1p_07989 63 2.48   67% 6 of 9    100%10 of 10  ha1p_08588 64 4.17   90% 9 of 10   70% 7 of 10 ha1p_09700 650.66   33% 3 of 9    100% 9 of 9  ha1p_104458 66 4.08   80% 8 of 10  90% 9 of 10 ha1p_105287 67 1.61   70% 7 of 10   80% 8 of 10 ha1p_1070268 1.14   50% 5 of 10   80% 8 of 10 ha1p_108469 69 1.72   90% 9 of 10  80% 8 of 10 ha1p_108849 70 3.63   80% 8 of 10   100% 9 of 9 ha1p_11016 71 2.28   90% 9 of 10   90% 9 of 10 ha1p_11023 72 2.04   90%9 of 10   80% 8 of 10 ha1p_12974 73 0.68   30% 3 of 10   100% 10 of 10 ha1p_16027 74 2.03   50% 5 of 10   90% 9 of 10 ha1p_16066 75 2.09   80%8 of 10   60% 6 of 10 ha1p_18911 76 2.52   78% 7 of 9    90% 9 of 10ha1p_19254 77 3.07   100% 10 of 10    80% 8 of 10 ha1p_19853 78 1.74  50% 5 of 10   100% 10 of 10  ha1p_22257 79 0.96   50% 5 of 10   80% 8of 10 ha1p_22519 80 2.62   70% 7 of 10   90% 9 of 10 ha1p_31800 81 3.8  60% 6 of 10   100% 10 of 10  ha1p_33290 82 2.56   90% 9 of 10   90% 9of 10 ha1p_37635 83 6   100% 10 of 10     0% 0 of 10 ha1p_39189 84 1.29  100% 9 of 9    80% 8 of 10 ha1p_39511 85 1.99   70% 7 of 10   90% 9 of10 ha1p_39752 86 3.01   40% 4 of 10   90% 9 of 10 ha1p_60945 87 1.11  100% 10 of 10    70% 7 of 10 ha1p_62183 88 2.58   60% 6 of 10   80% 8of 10 ha1p_69418 89 1.68   70% 7 of 10   80% 8 of 10 ha1p_71224 90 2.42  70% 7 of 10   90% 9 of 10 ha1p_74221 91 0.98   88% 7 of 8    67% 6 of9  ha1p_76289 92 1.84   80% 8 of 10   100% 9 of 9  ha1p_81050 93 5.74  60% 6 of 10   90% 9 of 10 ha1p_81674 94 2.3   60% 6 of 10   100% 10 of10  ha1p_86355 95 0.67   50% 5 of 10   80% 8 of 10 ha1p_98491 96 1.63  50% 5 of 10   80% 8 of 10 ha1p_99426 97 2.21   50% 5 of 10   100% 10of 10  Threshold: Average dCt value established by ROC curve analysis asoptimal threshold for distinguishing tumor and adjacent normal tissues.Sensitivity: % of positive (i.e., methylation score above Threshold forgain of methylation markers or below Threshold for loss of methylationmarkers) tumors. Pos. of Total: Number of positive tumors relative tothe total number of tumors analyzed. Specificity: % of negative (i.e.,methylation score below Threshold for gain of methylation markers orabove Threshold for loss of methylation markers) adjacent normalsamples. Neg. of Total: Number of negative adjacent normal samplesrelative to the total number of adjacent normal samples analyzed.

TABLE 17 Sensitivity and Specificity of differentially methylated lociin endometrial tumors relative to adjacent histological normalendometrial tissue. Pos. of Neg. of Feature Name Locus Number ThresholdSensitivity Total Specificity Total CHR01P001976799 1 2.95 78.57% 11 of14 100.00%  9 of 9 CHR01P026794862 2 1.565 50.00% 2 of 4 100.00%  1 of 1CHR01P043164342 3 3.105 92.86% 13 of 14 100.00%  9 of 9 CHR01P0631549994 0.705 85.71% 12 of 14 77.78% 7 of 9 CHR01P204123050 5 2.55 40.00%  4of 10 100.00%  5 of 5 CHR01P206905110 6 3.385 92.86% 13 of 14 100.00%  9of 9 CHR01P225608458 7 2.025 57.14%  8 of 14 88.89% 8 of 9CHR02P005061785 8 1.17 92.86% 13 of 14 88.89% 8 of 9 CHR02P042255672 91.175 78.57% 11 of 14 100.00%  9 of 9 CHR02P223364582 10 2.145 100.00% 14 of 14 66.67% 6 of 9 CHR03P027740753 11 0.985 85.71% 12 of 14 100.00% 9 of 9 CHR03P052525960 12 1.84 71.43% 10 of 14 88.89% 8 of 9CHR03P069745999 13 1.25 85.71% 12 of 14 44.44% 4 of 9 CHR05P059799713 141.285 71.43% 10 of 14 88.89% 8 of 9 CHR05P059799813 15 1.28 78.57% 11 of14 77.78% 7 of 9 CHR05P177842690 16 1.72 78.57% 11 of 14 88.89% 8 of 9CHR06P010694062 17 3.215 50.00%  7 of 14 100.00%  9 of 9 CHR06P02633331818 1.895 92.86% 13 of 14 100.00%  9 of 9 CHR08P102460854 19 1.44 92.86%13 of 14 88.89% 8 of 9 CHR08P102461254 20 1.635 100.00%  14 of 14100.00%  9 of 9 CHR08P102461554 21 1.97 100.00%  14 of 14 88.89% 8 of 9CHR09P000107988 22 1.52 92.86% 13 of 14 88.89% 8 of 9 CHR09P021958839 231.27 100.00%  14 of 14 66.67% 6 of 9 CHR09P131048752 24 2.72 71.43% 10of 14 77.78% 7 of 9 CHR10P118975684 25 0.505 35.71%  5 of 14 88.89% 8 of9 CHR11P021861414 26 5.925 78.57% 11 of 14 100.00%  9 of 9CHR12P004359362 27 2.28 92.86% 13 of 14 100.00%  9 of 9 CHR12P01600123128 1.635 100.00%  14 of 14 100.00%  9 of 9 CHR14P018893344 29 1.56571.43% 10 of 14 100.00%  9 of 9 CHR14P093230340 30 2.235 78.57% 11 of 1477.78% 7 of 9 CHR16P000373719 31 2.88 100.00%  10 of 10 80.00% 4 of 5CHR16P066389027 32 1.325 85.71% 12 of 14 50.00% 4 of 8 CHR16P08331965433 2.49 100.00%  14 of 14 88.89% 8 of 9 CHR18P019705147 34 3.36 92.86%13 of 14 100.00%  9 of 9 CHR19P018622408 35 1.97 71.43% 10 of 14100.00%  9 of 9 CHR19P051892823 36 1.11 85.71% 6 of 7 100.00%  6 of 6CHRXP013196410 37 1.205 92.31% 12 of 13 77.78% 7 of 9 CHRXP013196870 381.32 78.57% 11 of 14 71.43% 5 of 7 ha1p16_00179_l50 39 1.325 100.00%  14of 14 77.78% 7 of 9 ha1p16_00182_l50 40 0.985 92.86% 13 of 14 66.67% 6of 9 ha1p16_00257_l50 41 1.06 85.71% 12 of 14 77.78% 7 of 9ha1p_12601_l50 42 3.35 100.00%  14 of 14 88.89% 8 of 9 ha1p_17147_l50 432.68 100.00%  14 of 14 100.00%  9 of 9 ha1p_42350_l50 44 2.175 38.46%  5of 13 100.00%  7 of 7 ha1p_44897_l50 45 4.065 78.57% 11 of 14 44.44% 4of 9 ha1p_61253_l50 46 1.115 100.00%  6 of 6 50.00% 1 of 2CHR01P001005050 47 1.75 80.00%  8 of 10 100.00%  7 of 7 CHR16P00115747948 — — — — — ha1g_00681 49 1.74   50%  7 of 14   100% 9 of 9 ha1g_0196650 2.26   71% 10 of 14   78% 7 of 9 ha1g_02153 51 0.68   93% 13 of 14  78% 7 of 9 ha1g_02319 52 0.62   36%  5 of 14   100% 9 of 9 ha1g_0233553 1.77   69%  9 of 13   44% 4 of 9 ha1p16_00182 54 0.89   79% 11 of 14  78% 7 of 9 ha1p16_00185 55 0.86   71% 10 of 14   89% 8 of 9ha1p16_00193 56 1.67   85% 11 of 13   78% 7 of 9 ha1p16_00259 57 1.94  100% 14 of 14   78% 7 of 9 ha1p_02799 58 4.32   93% 13 of 14   78% 7of 9 ha1p_03567 59 1.79   79% 11 of 14   89% 8 of 9 ha1p_03671 60 0.83  64%  9 of 14   78% 7 of 9 ha1p_05803 61 0.66   93% 13 of 14   89% 8 of9 ha1p_07131 62 6   86% 12 of 14   100% 9 of 9 ha1p_07989 63 3.79   36% 5 of 14   100% 9 of 9 ha1p_08588 64 6   93% 13 of 14   100% 9 of 9ha1p_09700 65 0.82   100% 13 of 13   25% 2 of 8 ha1p_104458 66 3.93  29%  4 of 14   100% 9 of 9 ha1p_105287 67 2.99   100% 14 of 14   100%9 of 9 ha1p_10702 68 0.75   50%  7 of 14   100% 8 of 8 ha1p_108469 691.43   64%  9 of 14   67% 6 of 9 ha1p_108849 70 2.92   79% 11 of 14  89% 8 of 9 ha1p_11016 71 4.13   50%  7 of 14   100% 9 of 9 ha1p_1102372 1.61   64%  9 of 14   100% 9 of 9 ha1p_12974 73 0.51    0%  0 of 14  89% 8 of 9 ha1p_16027 74 1.93   64%  9 of 14   56% 5 of 9 ha1p_1606675 0.51   79% 11 of 14   78% 7 of 9 ha1p_18911 76 2.53   43%  6 of 14  89% 8 of 9 ha1p_19254 77 5.53   79% 11 of 14   100% 9 of 9 ha1p_1985378 0.85   79% 11 of 14   89% 8 of 9 ha1p_22257 79 2.22   43%  6 of 14  100% 9 of 9 ha1p_22519 80 2.02   64%  9 of 14   100% 9 of 9 ha1p_3180081 3.52   57%  8 of 14   89% 8 of 9 ha1p_33290 82 1.24   71% 10 of 14  89% 8 of 9 ha1p_37635 83 6    7%  1 of 14   100% 9 of 9 ha1p_39189 840.63   86% 12 of 14   89% 8 of 9 ha1p_39511 85 3.91   71% 10 of 14   56%5 of 9 ha1p_39752 86 1.66   86% 12 of 14   78% 7 of 9 ha1p_60945 87 0.86  86% 12 of 14   33% 3 of 9 ha1p_62183 88 4.01   71% 10 of 14   100% 9of 9 ha1p_69418 89 2.53   50%  7 of 14   100% 9 of 9 ha1p_71224 90 1.46  71% 10 of 14   78% 7 of 9 ha1p_74221 91 0.88   85% 11 of 13   63% 5 of8 ha1p_76289 92 0.74   71% 10 of 14   78% 7 of 9 ha1p_81050 93 5.92  79% 11 of 14   100% 9 of 9 ha1p_81674 94 0.82   86% 12 of 14   89% 8of 9 ha1p_86355 95 1.19   79% 11 of 14   75% 6 of 8 ha1p_98491 96 2.11  62%  8 of 13   100% 9 of 9 ha1p_99426 97 0.66   79% 11 of 14   89% 8of 9 Threshold: Average dCt value established by ROC curve analysis asoptimal threshold for distinguishing tumor and adjacent normal tissues.Sensitivity: % of positive (i.e., methylation score above Threshold forgain of methylation markers or below Threshold for loss of methylationmarkers) tumors. Pos. of Total: Number of positive tumors relative tothe total number of tumors analyzed. Specificity: % of negative (i.e.,methylation score below Threshold for gain of methylation markers orabove Threshold for loss of methylation markers) adjacent normalsamples. Neg. of Total: Number of negative adjacent normal samplesrelative to the total number of adjacent normal samples analyzed.

TABLE 18 Sensitivity and Specificity of differentially methylated lociin esophageal tumors relative to adjacent histological normal esophagealtissue. Locus Pos. of Neg. of Feature Name Number Threshold SensitivityTotal Specificity Total CHR01P001976799 1 6 100.00%    9 of 9 0.00%   0of 10 CHR01P026794862 2 1.075 12.50%   1 of 8 100.00%    6 of 6 CHR01P043164342 3 5.84 66.67%   6 of 9 100.00%    10 of 10 CHR01P063154999 4 1.02 100.00%    9 of 9 50.00%   5 of 10CHR01P204123050 5 1.515 62.50%   5 of 8 77.78%   7 of 9  CHR01P2069051106 1.935 88.89%   8 of 9 66.67%   6 of 9  CHR01P225608458 7 1.52100.00%    9 of 9 70.00%   7 of 10 CHR02P005061785 8 3.345 55.56%   5 of9 80.00%   8 of 10 CHR02P042255672 9 3.095 88.89%   8 of 9 70.00%   7 of10 CHR02P223364582 10 1.765 88.89%   8 of 9 80.00%   8 of 10CHR03P027740753 11 0.97 100.00%    9 of 9 80.00%   8 of 10CHR03P052525960 12 1.93 55.56%   5 of 9 90.00%   9 of 10 CHR03P06974599913 3.435 77.78%   7 of 9 80.00%   8 of 10 CHR05P059799713 14 1.3877.78%   7 of 9 70.00%   7 of 10 CHR05P059799813 15 1.57 55.56%   5 of 990.00%   9 of 10 CHR05P177842690 16 2.435 62.50%   5 of 8 55.56%   5 of9  CHR06P010694062 17 2.07 77.78%   7 of 9 77.78%   7 of 9 CHR06P026333318 18 1.34 88.89%   8 of 9 60.00%   6 of 10 CHR08P10246085419 0.555 66.67%   6 of 9 60.00%   6 of 10 CHR08P102461254 20 0.8662.50%   5 of 8 70.00%   7 of 10 CHR08P102461554 21 0.905 88.89%   8 of9 40.00%   4 of 10 CHR09P000107988 22 1.025 100.00%    9 of 9 60.00%   6of 10 CHR09P021958839 23 0.965 100.00%    9 of 9 40.00%   4 of 10CHR09P131048752 24 3.61 77.78%   7 of 9 90.00%   9 of 10 CHR10P11897568425 1.455 66.67%   6 of 9 100.00%    10 of 10 CHR11P021861414 26 4.49100.00%    9 of 9 80.00%   8 of 10 CHR12P004359362 27 2.085 66.67%   6of 9 90.00%   9 of 10 CHR12P016001231 28 0.855 50.00%   4 of 8 90.00%  9 of 10 CHR14P018893344 29 2.14 100.00%    9 of 9 90.00%   9 of 10CHR14P093230340 30 2.035 100.00%    9 of 9 90.00%   9 of 10CHR16P000373719 31 0.83 87.50%   7 of 8 66.67%   6 of 9  CHR16P06638902732 0.75 100.00%    9 of 9 11.11%   1 of 9  CHR16P083319654 33 2.14555.56%   5 of 9 90.00%   9 of 10 CHR18P019705147 34 2.245 88.89%   8 of9 50.00%   4 of 8  CHR19P018622408 35 1.78 100.00%    9 of 9 60.00%   6of 10 CHR19P051892823 36 2.295 25.00%   1 of 4 100.00%    5 of 5 CHRXP013196410 37 1.615 100.00%    9 of 9 40.00%   4 of 10CHRXP013196870 38 1.945 88.89%   8 of 9 50.00%   5 of 10ha1p16_00179_l50 39 1.405 44.44%   4 of 9 90.00%   9 of 10ha1p16_00182_l50 40 0.995 66.67%   6 of 9 77.78%   7 of 9 ha1p16_00257_l50 41 0.8 88.89%   8 of 9 40.00%   4 of 10 ha1p_12601_l5042 1.125 88.89%   8 of 9 60.00%   6 of 10 ha1p_17147_l50 43 1.02566.67%   6 of 9 80.00%   8 of 10 ha1p_42350_l50 44 2.885 100.00%    8 of8 88.89%   8 of 9  ha1p_44897_l50 45 1.715 100.00%    9 of 9 50.00%   5of 10 ha1p_61253_l50 46 1.57 77.78%   7 of 9 88.89%   8 of 9 CHR01P001005050 47 1.57 55.56%   5 of 9 66.67%   6 of 9  CHR16P00115747948 4.79 100.00%    1 of 1 66.67%   2 of 3  ha1g_00681 49 0.69 89% 8 of 980% 8 of 10 ha1g_01966 50 1.74 100%  9 of 9 70% 7 of 10 ha1g_02153 511.42 78% 7 of 9 80% 8 of 10 ha1g_02319 52 1.01 89% 8 of 9 80% 8 of 10ha1g_02335 53 3.54 89% 8 of 9 40% 4 of 10 ha1p16_00182 54 0.78 89% 8 of9 70% 7 of 10 ha1p16_00185 55 0.85 89% 8 of 9 50% 5 of 10 ha1p16_0019356 1.45 89% 8 of 9 50% 5 of 10 ha1p16_00259 57 2.28 50% 4 of 8 80% 8 of10 ha1p_02799 58 2.63 56% 5 of 9 100%  10 of 10  ha1p_03567 59 1.09 78%7 of 9 80% 8 of 10 ha1p_03671 60 0.91 100%  9 of 9 90% 9 of 10ha1p_05803 61 1.37 100%  9 of 9 90% 9 of 10 ha1p_07131 62 5.25 100%  9of 9 80% 8 of 10 ha1p_07989 63 2.79 83% 5 of 6 80% 8 of 10 ha1p_08588 645.89 89% 8 of 9 70% 7 of 10 ha1p_09700 65 — — — — — ha1p_14458 66 3.2378% 7 of 9 70% 7 of 10 ha1p_105287 67 1.49 56% 5 of 9 90% 9 of 10ha1p_10702 68 0.68 56% 5 of 9 100%  10 of 10  ha1p_108469 69 1.7 33% 3of 9 100%  9 of 9  ha1p_108849 70 2.84 89% 8 of 9 70% 7 of 10 ha1p_1101671 2.81 89% 8 of 9 70% 7 of 10 ha1p_11023 72 2.33 56% 5 of 9 90% 9 of 10ha1p_12974 73 0.79 67% 6 of 9 80% 8 of 10 ha1p_16027 74 1.15 67% 6 of 990% 9 of 10 ha1p_16066 75 1.17 89% 8 of 9 100%  10 of 10  ha1p_18911 762.13 88% 7 of 8 80% 8 of 10 ha1p_19254 77 3.38 89% 8 of 9 80% 8 of 10ha1p_19853 78 0.76 100%  9 of 9 70% 7 of 10 ha1p_22257 79 1.64 89% 8 of9 90% 9 of 10 ha1p_22519 80 1.76 89% 8 of 9 90% 9 of 10 ha1p_31800 813.47 80% 4 of 5 80% 8 of 10 ha1p_33290 82 1.26 100%  9 of 9 60% 6 of 10ha1p_37635 83 6 100%  9 of 9  0% 0 of 10 ha1p_39189 84 1.75 100%  9 of 9100%  10 of 10  ha1p_39511 85 3.21 11% 1 of 9 100%  10 of 10  ha1p_3975286 2.3 100%  9 of 9 70% 7 of 10 ha1p_60945 87 1.81 89% 8 of 9 90% 9 of10 ha1p_62183 88 2.84 33% 3 of 9 100%  10 of 10  ha1p_69418 89 2.29 78%7 of 9 80% 8 of 10 ha1p_71224 90 1.69 78% 7 of 9 60% 6 of 10 ha1p_7422191 1.42 88% 7 of 8 70% 7 of 10 ha1p_76289 92 1.45 67% 6 of 9 100%  8 of8  ha1p_81050 93 6 89% 8 of 9 80% 8 of 10 ha1p_81674 94 1.98 67% 6 of 990% 9 of 10 ha1p_86355 95 2.09 33% 3 of 9 89% 8 of 9  ha1p_98491 96 2.8944% 4 of 9 80% 8 of 10 ha1p_99426 97 1.24 89% 8 of 9 90% 9 of 10Threshold: Average dCt value established by ROC curve analysis asoptimal threshold for distinguishing tumor and adjacent normal tissues.Sensitivity: % of positive (i.e., methylation score above Threshold forgain of methylation markers or below Threshold for loss of methylationmarkers) tumors. Pos. of Total: Number of positive tumors relative tothe total number of tumors analyzed. Specificity: % of negative (i.e.,methylation score below Threshold for gain of methylation markers orabove Threshold for loss of methylation markers) adjacent normalsamples. Neg. of Total: Number of negative adjacent normal samplesrelative to the total number of adjacent normal samples analyzed.

TABLE 19 Sensitivity and Specificity of differentially methylated lociin head and neck tumors relative to adjacent histological normal headand neck tissue. Locus Pos. of Neg. of Feature Name Number ThresholdSensitivity Total Specificity Total CHR01P001976799 1 6 87.50%   7 of 840.00%   2 of 5 CHR01P026794862 2 0.615 57.14%   4 of 7 100.00%    1 of1 CHR01P043164342 3 4.85 77.78%   7 of 9 100.00%    5 of 5CHR01P063154999 4 0.875 100.00%    9 of 9 80.00%   4 of 5CHR01P204123050 5 1.605 44.44%   4 of 9 80.00%   4 of 5 CHR01P2069051106 1.92 55.56%   5 of 9 80.00%   4 of 5 CHR01P225608458 7 1.8 77.78%   7of 9 100.00%    5 of 5 CHR02P005061785 8 3.455 66.67%   6 of 9 80.00%  4 of 5 CHR02P042255672 9 4.075 77.78%   7 of 9 80.00%   4 of 5CHR02P223364582 10 1.7 88.89%   8 of 9 100.00%    5 of 5 CHR03P02774075311 1.05 88.89%   8 of 9 100.00%    5 of 5 CHR03P052525960 12 2.6655.56%   5 of 9 100.00%    5 of 5 CHR03P069745999 13 4.04 77.78%   7 of9 100.00%    5 of 5 CHR05P059799713 14 1.595 66.67%   6 of 9 80.00%   4of 5 CHR05P059799813 15 1.745 57.14%   4 of 7 100.00%    5 of 5CHR05P177842690 16 1.12 100.00%    9 of 9 60.00%   3 of 5CHR06P010694062 17 2.695 66.67%   6 of 9 100.00%    5 of 5CHR06P026333318 18 2.335 77.78%   7 of 9 80.00%   4 of 5 CHR08P10246085419 0.625 55.56%   5 of 9 80.00%   4 of 5 CHR08P102461254 20 0.6488.89%   8 of 9 40.00%   2 of 5 CHR08P102461554 21 0.77 33.33%   3 of 980.00%   4 of 5 CHR09P000107988 22 0.97 100.00%    9 of 9 100.00%    5of 5 CHR09P021958839 23 1.09 88.89%   8 of 9 100.00%    5 of 5CHR09P131048752 24 4.29 77.78%   7 of 9 100.00%    5 of 5CHR10P118975684 25 1.405 87.50%   7 of 8 80.00%   4 of 5 CHR11P02186141426 4.21 88.89%   8 of 9 80.00%   4 of 5 CHR12P004359362 27 1.06577.78%   7 of 9 60.00%   3 of 5 CHR12P016001231 28 1.25 66.67%   6 of 960.00%   3 of 5 CHR14P018893344 29 2.43 88.89%   8 of 9 100.00%    5 of5 CHR14P093230340 30 1.26 100.00%    9 of 9 80.00%   4 of 5CHR16P000373719 31 1.215 100.00%    6 of 6 75.00%   3 of 4CHR16P066389027 32 1.645 77.78%   7 of 9 60.00%   3 of 5 CHR16P08331965433 3.81 100.00%    9 of 9 20.00%   1 of 5 CHR18P019705147 34 1.7355.56%   5 of 9 80.00%   4 of 5 CHR19P018622408 35 2.245 88.89%   8 of 980.00%   4 of 5 CHR19P051892823 36 3.875 80.00%   4 of 5 100.00%    3 of3 CHRXP013196410 37 1.72 100.00%    9 of 9 40.00%   2 of 5CHRXP013196870 38 1.38 88.89%   8 of 9 40.00%   2 of 5 ha1p16_00179_l5039 1.105 88.89%   8 of 9 100.00%    5 of 5 ha1p16_00182_l50 40 0.735100.00%    9 of 9 100.00%    5 of 5 ha1p16_00257_l50 41 1.495 77.78%   7of 9 100.00%    5 of 5 ha1p_12601_l50 42 1.035 66.67%   6 of 9 60.00%  3 of 5 ha1p_17147_l50 43 1.53 37.50%   3 of 8 100.00%    5 of 5ha1p_42350_l50 44 4.505 100.00%    7 of 7 80.00%   4 of 5 ha1p_44897_l5045 2.59 100.00%    8 of 8 80.00%   4 of 5 ha1p_61253_l50 46 1.1988.89%   8 of 9 75.00%   3 of 4 CHR01P001005050 47 1.205 71.43%   5 of 7100.00%    3 of 3 CHR16P001157479 48 — — — — — ha1g_00681 49 0.73 100% 9 of 9 40% 2 of 5 ha1g_01966 50 1.59 100%  9 of 9 50% 2 of 4 ha1g_0215351 1.42 78% 7 of 9 100%  5 of 5 ha1g_02319 52 0.86 89% 8 of 9 60% 3 of 5ha1g_02335 53 3.52 67% 6 of 9 100%  5 of 5 ha1p16_00182 54 0.88 89% 8 of9 100%  5 of 5 ha1p16_00185 55 0.99 89% 8 of 9 80% 4 of 5 ha1p16_0019356 1.75 89% 8 of 9 80% 4 of 5 ha1p16_00259 57 2.44 67% 6 of 9 80% 4 of 5ha1p_02799 58 2.27 100%  9 of 9 40% 2 of 5 ha1p_03567 59 1.61 89% 8 of 940% 2 of 5 ha1p_03671 60 0.54 100%  9 of 9 100%  4 of 4 ha1p_05803 611.88 78% 7 of 9 80% 4 of 5 ha1p_07131 62 3.46 78% 7 of 9 100%  5 of 5ha1p_07989 63 2.09 83% 5 of 6 80% 4 of 5 ha1p_08588 64 5.5 67% 6 of 960% 3 of 5 ha1p_09700 65 0.61 50% 4 of 8 80% 4 of 5 ha1p_104458 66 4.378% 7 of 9 100%  5 of 5 ha1p_105287 67 1.8 67% 6 of 9 80% 4 of 5ha1p_10702 68 1.79 33% 3 of 9 100%  5 of 5 ha1p_108469 69 1.85 22% 2 of9 100%  5 of 5 ha1p_108849 70 2.7 67% 6 of 9 80% 4 of 5 ha1p_11016 713.99 100%  9 of 9 80% 4 of 5 ha1p_11023 72 2.14 100%  9 of 9 60% 3 of 5ha1p_12974 73 0.57 78% 7 of 9 60% 3 of 5 ha1p_16027 74 0.57 100%  9 of 960% 3 of 5 ha1p_16066 75 0.76 89% 8 of 9 80% 4 of 5 ha1p_18911 76 2.7389% 8 of 9 60% 3 of 5 ha1p_19254 77 2.44 67% 6 of 9 100%  5 of 5ha1p_19853 78 0.7 89% 8 of 9 100%  5 of 5 ha1p_22257 79 1.86 78% 7 of 9100%  5 of 5 ha1p_22519 80 2.18 56% 5 of 9 100%  5 of 5 ha1p_31800 812.79 100%  7 of 7 40% 2 of 5 ha1p_33290 82 1.86 78% 7 of 9 100%  5 of 5ha1p_37635 83 6 100%  8 of 8  0% 0 of 5 ha1p_39189 84 0.97 86% 6 of 780% 4 of 5 ha1p_39511 85 1.88 89% 8 of 9 40% 2 of 5 ha1p_39752 86 3.0256% 5 of 9 100%  5 of 5 ha1p_60945 87 2.31 78% 7 of 9 80% 4 of 5ha1p_62183 88 4.34 67% 6 of 9 60% 3 of 5 ha1p_69418 89 2.22 43% 3 of 7100%  5 of 5 ha1p_71224 90 1.89 78% 7 of 9 80% 4 of 5 ha1p_74221 91 2.0867% 6 of 9 80% 4 of 5 ha1p_76289 92 1.5 78% 7 of 9 100%  5 of 5ha1p_81050 93 5.46 78% 7 of 9 80% 4 of 5 ha1p_81674 94 1.88 67% 6 of 980% 4 of 5 ha1p_86355 95 1.18 100%  9 of 9 50% 2 of 4 ha1p_98491 96 2.2722% 2 of 9 100%  5 of 5 ha1p_99426 97 1.13 78% 7 of 9 100%  5 of 5Threshold: Average dCt value established by ROC curve analysis asoptimal threshold for distinguishing tumor and adjacent normal tissues.Sensitivity: % of positive (i.e., methylation score above Threshold forgain of methylation markers or below Threshold for loss of methylationmarkers) tumors. Pos. of Total: Number of positive tumors relative tothe total number of tumors analyzed. Specificity: % of negative (i.e.,methylation score below Threshold for gain of methylation markers orabove Threshold for loss of methylation markers) adjacent normalsamples. Neg. of Total: Number of negative adjacent normal samplesrelative to the total number of adjacent normal samples analyzed.

TABLE 20 Sensitivity and Specificity of differentially methylated lociin liver tumors relative to adjacent histological normal liver tissue.Locus Pos. of Neg. of Feature Name Number Threshold Sensitivity TotalSpecificity Total CHR01P001976799 1 6 100.00%    8 of 8 0.00%   0 of 9CHR01P026794862 2 1.295 42.86%   3 of 7 100.00%    9 of 9CHR01P043164342 3 5.27 87.50%   7 of 8 88.89%   8 of 9 CHR01P063154999 41.4 77.78%   7 of 9 71.43%   5 of 7 CHR01P204123050 5 2.08 33.33%   3 of9 88.89%   8 of 9 CHR01P206905110 6 5.88 33.33%   3 of 9 100.00%    9 of9 CHR01P225608458 7 2.62 55.56%   5 of 9 88.89%   8 of 9 CHR02P0050617858 4.8 55.56%   5 of 9 100.00%    9 of 9 CHR02P042255672 9 3.835 77.78%  7 of 9 75.00%   6 of 8 CHR02P223364582 10 1.73 55.56%   5 of 9 77.78%  7 of 9 CHR03P027740753 11 1.47 55.56%   5 of 9 88.89%   8 of 9CHR03P052525960 12 4.685 44.44%   4 of 9 100.00%    9 of 9CHR03P069745999 13 4.745 55.56%   5 of 9 100.00%    8 of 8CHR05P059799713 14 3.63 50.00%   4 of 8 87.50%   7 of 8 CHR05P05979981315 2.405 44.44%   4 of 9 100.00%    8 of 8 CHR05P177842690 16 2.1266.67%   6 of 9 100.00%    9 of 9 CHR06P010694062 17 4.24 66.67%   6 of9 66.67%   6 of 9 CHR06P026333318 18 5.665 55.56%   5 of 9 88.89%   8 of9 CHR08P102460854 19 1.305 87.50%   7 of 8 55.56%   5 of 9CHR08P102461254 20 1.985 66.67%   6 of 9 77.78%   7 of 9 CHR08P10246155421 1.545 88.89%   8 of 9 55.56%   5 of 9 CHR09P000107988 22 1.70533.33%   3 of 9 88.89%   8 of 9 CHR09P021958839 23 2.335 66.67%   6 of 977.78%   7 of 9 CHR09P131048752 24 5.305 33.33%   3 of 9 100.00%    9 of9 CHR10P118975684 25 1.59 50.00%   3 of 6 100.00%    7 of 7CHR11P021861414 26 4.58 33.33%   3 of 9 100.00%    9 of 9CHR12P004359362 27 1.855 77.78%   7 of 9 88.89%   8 of 9 CHR12P01600123128 1.515 66.67%   6 of 9 55.56%   5 of 9 CHR14P018893344 29 3.4566.67%   6 of 9 77.78%   7 of 9 CHR14P093230340 30 1.58 66.67%   6 of 977.78%   7 of 9 CHR16P000373719 31 4.565 60.00%   3 of 5 75.00%   3 of 4CHR16P066389027 32 1.955 88.89%   8 of 9 55.56%   5 of 9 CHR16P08331965433 1.24 77.78%   7 of 9 88.89%   8 of 9 CHR18P019705147 34 3.76100.00%    9 of 9 100.00%    6 of 6 CHR19P018622408 35 3.325 66.67%   6of 9 66.67%   6 of 9 CHR19P051892823 36 4.08 100.00%    2 of 2100.00%    1 of 1 CHRXP013196410 37 1.215 50.00%   4 of 8 85.71%   6 of7 CHRXP013196870 38 1.465 75.00%   6 of 8 55.56%   5 of 9ha1p16_00179_l50 39 2.28 75.00%   6 of 8 62.50%   5 of 8ha1p16_00182_l50 40 1.775 77.78%   7 of 9 77.78%   7 of 9ha1p16_00257_l50 41 0.98 88.89%   8 of 9 66.67%   6 of 9 ha1p_12601_l5042 0.915 44.44%   4 of 9 100.00%    8 of 8 ha1p_17147_l50 43 1.17544.44%   4 of 9 88.89%   8 of 9 ha1p_42350_l50 44 1.975 42.86%   3 of 7100.00%    6 of 6 ha1p_44897_l50 45 3.59 66.67%   6 of 9 75.00%   6 of 8ha1p_61253_l50 46 4.055 77.78%   7 of 9 88.89%   8 of 9 CHR01P00100505047 3.3 100.00%    9 of 9 77.78%   7 of 9 CHR16P001157479 48 6 25.00%   2of 8 100.00%    5 of 5 ha1g_00681 49 2.66 89% 8 of 9 78% 7 of 9ha1g_01966 50 3.07 38% 3 of 8 100%  9 of 9 ha1g_02153 51 0.72 56% 5 of 978% 7 of 9 ha1g_02319 52 2.1 56% 5 of 9 89% 8 of 9 ha1g_02335 53 2.6250% 4 of 8 89% 8 of 9 ha1p16_00182 54 1.73 89% 8 of 9 78% 7 of 9ha1p16_00185 55 1.65 67% 6 of 9 100%  9 of 9 ha1p16_00193 56 2.73 56% 5of 9 78% 7 of 9 ha1p16_00259 57 3.77 78% 7 of 9 89% 8 of 9 ha1p_02799 582.5 78% 7 of 9 86% 6 of 7 ha1p_03567 59 0.68 33% 3 of 9 89% 8 of 9ha1p_03671 60 2.39 44% 4 of 9 78% 7 of 9 ha1p_05803 61 2.52 56% 5 of 989% 8 of 9 ha1p_07131 62 2.4 89% 8 of 9 89% 8 of 9 ha1p_07989 63 1.5 88%7 of 8 56% 5 of 9 ha1p_08588 64 3.26 89% 8 of 9 89% 8 of 9 ha1p_09700 650.81 50% 4 of 8 100%  9 of 9 ha1p_104458 66 3.8 67% 6 of 9 56% 5 of 9ha1p_105287 67 3.67 44% 4 of 9 100%  8 of 8 ha1p_10702 68 0.53 56% 5 of9 67% 6 of 9 ha1p_108469 69 2.31 44% 4 of 9 89% 8 of 9 ha1p_108849 703.94 56% 5 of 9 78% 7 of 9 ha1p_11016 71 4.36 44% 4 of 9 89% 8 of 9ha1p_11023 72 2.59 33% 3 of 9 100%  9 of 9 ha1p_12974 73 0.65 89% 8 of 922% 2 of 9 ha1p_16027 74 1.51 63% 5 of 8 89% 8 of 9 ha1p_16066 75 1.4367% 6 of 9 78% 7 of 9 ha1p_18911 76 2.32 56% 5 of 9 67% 6 of 9ha1p_19254 77 1.79 100%  7 of 7 89% 8 of 9 ha1p_19853 78 0.71 50% 4 of 878% 7 of 9 ha1p_22257 79 2.56 67% 6 of 9 89% 8 of 9 ha1p_22519 80 2.8356% 5 of 9 78% 7 of 9 ha1p_31800 81 2.81 67% 6 of 9 56% 5 of 9ha1p_33290 82 0.89 44% 4 of 9 100%  9 of 9 ha1p_37635 83 6 44% 4 of 989% 8 of 9 ha1p_39189 84 1.29 67% 6 of 9 89% 8 of 9 ha1p_39511 85 2.01100%  9 of 9 56% 5 of 9 ha1p_39752 86 1.09 44% 4 of 9 100%  9 of 9ha1p_60945 87 1.74 44% 4 of 9 78% 7 of 9 ha1p_62183 88 2.48 67% 6 of 9100%  9 of 9 ha1p_69418 89 6 56% 5 of 9 89% 8 of 9 ha1p_71224 90 0.9650% 4 of 8 89% 8 of 9 ha1p_74221 91 2.22 22% 2 of 9 100%  9 of 9ha1p_76289 92 1.55 88% 7 of 8 78% 7 of 9 ha1p_81050 93 5.95 56% 5 of 9100%  9 of 9 ha1p_81674 94 3.45 22% 2 of 9 100%  9 of 9 ha1p_86355 951.61 56% 5 of 9 89% 8 of 9 ha1p_98491 96 2.63 56% 5 of 9 100%  9 of 9ha1p_99426 97 1.06 100%  9 of 9 78% 7 of 9 Threshold: Average dCt valueestablished by ROC curve analysis as optimal threshold fordistinguishing tumor and adjacent normal tissues. Sensitivity: % ofpositive (i.e., methylation score above Threshold for gain ofmethylation markers or below Threshold for loss of methylation markers)tumors. Pos. of Total: Number of positive tumors relative to the totalnumber of tumors analyzed. Specificity: % of negative (i.e., methylationscore below Threshold for gain of methylation markers or above Thresholdfor loss of methylation markers) adjacent normal samples. Neg. of Total:Number of negative adjacent normal samples relative to the total numberof adjacent normal samples analyzed.

TABLE 21 Sensitivity and Specificity of differentially methylated lociin lung tumors relative to adjacent histological normal lung tissue.Locus Pos. of Neg. of Feature Name Number Threshold Sensitivity TotalSpecificity Total CHR01P001976799 1 6 100.00%    20 of 20 0.00%    0 of20 CHR01P026794862 2 0.855 47.06%    8 of 17 88.24%   15 of 17CHR01P043164342 3 2.25 80.00%   16 of 20 68.42%   13 of 19CHR01P063154999 4 1.005 90.00%   18 of 20 90.00%   18 of 20CHR01P204123050 5 0.835 100.00%    20 of 20 15.00%    3 of 20CHR01P206905110 6 1.655 55.00%   11 of 20 90.00%   18 of 20CHR01P225608458 7 1.485 90.00%   18 of 20 90.00%   18 of 20CHR02P005061785 8 3.955 78.95%   15 of 19 57.89%   11 of 19CHR02P042255672 9 4.045 85.00%   17 of 20 90.00%   18 of 20CHR02P223364582 10 1.82 75.00%   15 of 20 90.00%   18 of 20CHR03P027740753 11 1.19 90.00%   18 of 20 100.00%    18 of 18CHR03P052525960 12 2.2 30.00%    6 of 20 95.00%   19 of 20CHR03P069745999 13 4.23 70.00%   14 of 20 50.00%   10 of 20CHR05P059799713 14 2.075 50.00%   10 of 20 80.00%   16 of 20CHR05P059799813 15 2.715 30.00%    6 of 20 100.00%    18 of 18CHR05P177842690 16 2.145 55.00%   11 of 20 70.00%   14 of 20CHR06P010694062 17 3.31 80.00%   16 of 20 85.00%   17 of 20CHR06P026333318 18 3.605 80.00%   16 of 20 95.00%   19 of 20CHR08P102460854 19 0.955 5.00%    1 of 20 100.00%    20 of 20CHR08P102461254 20 0.57 50.00%   10 of 20 75.00%   15 of 20CHR08P102461554 21 0.53 40.00%    8 of 20 80.00%   16 of 20CHR09P000107988 22 1.44 60.00%   12 of 20 85.00%   17 of 20CHR09P021958839 23 1.525 75.00%   15 of 20 90.00%   18 of 20CHR09P131048752 24 3.285 90.00%   18 of 20 70.00%   14 of 20CHR10P118975684 25 1.14 85.00%   17 of 20 100.00%    20 of 20CHR11P021861414 26 4.05 70.00%   14 of 20 90.00%   18 of 20CHR12P004359362 27 2.155 70.00%   14 of 20 95.00%   19 of 20CHR12P016001231 28 1.705 65.00%   13 of 20 68.42%   13 of 19CHR14P018893344 29 2.5 85.00%   17 of 20 78.95%   15 of 19CHR14P093230340 30 1.465 89.47%   17 of 19 95.00%   19 of 20CHR16P000373719 31 1.36 62.50%   10 of 16 88.24%   15 of 17CHR16P066389027 32 1.195 57.89%   11 of 19 66.67%   12 of 18CHR16P083319654 33 1.895 80.00%   16 of 20 80.00%   16 of 20CHR18P019705147 34 3.865 40.00%    8 of 20 100.00%    20 of 20CHR19P018622408 35 2.105 84.21%   16 of 19 90.00%   18 of 20CHR19P051892823 36 1.095 83.33%   10 of 12 76.92%   10 of 13CHRXP013196410 37 3.725 45.00%    9 of 20 95.00%   19 of 20CHRXP013196870 38 3.12 60.00%   12 of 20 80.00%   16 of 20ha1p16_00179_l50 39 1.575 65.00%   13 of 20 100.00%    20 of 20ha1p16_00182_l50 40 1.07 85.00%   17 of 20 80.00%   16 of 20ha1p16_00257_l50 41 1.045 78.95%   15 of 19 90.00%   18 of 20ha1p_12601_l50 42 0.75 50.00%   10 of 20 85.00%   17 of 20ha1p_17147_l50 43 0.7 60.00%   12 of 20 85.00%   17 of 20 ha1p_42350_l5044 2.205 94.12%   16 of 17 73.68%   14 of 19 ha1p_44897_l50 45 2.2270.00%   14 of 20 65.00%   13 of 20 ha1p_61253_l50 46 1.895 77.78%   14of 18 83.33%   15 of 18 CHR01P001005050 47 0.52 63.16%   12 of 1964.71%   11 of 17 CHR16P001157479 48 — — — — — ha1g_00681 49 1.5 80%  8of 10 90%  9 of 10 ha1g_01966 50 1.37 87% 40 of 46 96% 46 of 48ha1g_02153 51 0.62 84% 38 of 45 90% 43 of 48 ha1g_02319 52 0.73 74% 35of 47 96% 45 of 47 ha1g_02335 53 1.68 80%  8 of 10 90%  9 of 10ha1p16_00182 54 1.18 65% 30 of 46 90% 43 of 48 ha1p16_00185 55 1.1 68%30 of 44 91% 40 of 44 ha1p16_00193 56 1.76 89% 40 of 45 68% 32 of 47ha1p16_00259 57 2.31 75% 33 of 44 93% 43 of 46 ha1p_02799 58 2.06 47% 22of 47 88% 42 of 48 ha1p_03567 59 1.14 78% 36 of 46 83% 39 of 47ha1p_03671 60 1.42 76% 35 of 46 65% 31 of 48 ha1p_05803 61 1.63 70%  7of 10 90%  9 of 10 ha1p_07131 62 3.88 83% 39 of 47 93% 43 of 46ha1p_07989 63 2.86 85% 40 of 47 90% 43 of 48 ha1p_08588 64 3.9 80% 37 of46 88% 42 of 48 ha1p_09700 65 1.01 56% 25 of 45 93% 43 of 46 ha1p_10445866 3.94 80%  8 of 10 89%  8 of 9 ha1p_105287 67 2.04 83% 40 of 48 73% 35of 48 ha1p_10702 68 0.5 66% 27 of 41 87% 39 of 45 ha1p_108469 69 0.9883% 38 of 46 89% 42 of 47 ha1p_108849 70 3.7 70%  7 of 10 100%  10 of 10ha1p_11016 71 2.88 70%  7 of 10 100%  10 of 10 ha1p_11023 72 3.41 100% 10 of 10 20%  2 of 10 ha1p_12974 73 0.59 36% 17 of 47 90% 43 of 48ha1p_16027 74 1.02 73% 35 of 48 92% 44 of 48 ha1p_16066 75 0.79 73% 35of 48 85% 41 of 48 ha1p_18911 76 2.87 70% 33 of 47 96% 45 of 47ha1p_19254 77 3.66 90%  9 of 10 90%  9 of 10 ha1p_19853 78 0.75 79% 37of 47 92% 44 of 48 ha1p_22257 79 1.12 80% 37 of 46 85% 41 of 48ha1p_22519 80 1.84 78% 35 of 45 94% 45 of 48 ha1p_31800 81 2.33 88% 42of 48 92% 44 of 48 ha1p_33290 82 1.71 87% 41 of 47 92% 44 of 48ha1p_37635 83 2.99 70%  7 of 10 80%  8 of 10 ha1p_39189 84 0.89 83% 39of 47 90% 38 of 42 ha1p_39511 85 2.36 78% 7 of 9 78% 7 of 9 ha1p_3975286 1.79 65% 30 of 46 81% 38 of 47 ha1p_60945 87 2.11 60%  6 of 10 100% 8 of 8 ha1p_62183 88 4.23 82% 37 of 45 83% 39 of 47 ha1p_69418 89 2.3685% 39 of 46 93% 43 of 46 ha1p_71224 90 1.98 77% 34 of 44 77% 30 of 39ha1p_74221 91 1.51 70%  7 of 10 100%  10 of 10 ha1p_76289 92 1.13 81% 34of 42 83% 33 of 40 ha1p_81050 93 5.6 92% 33 of 36 87% 40 of 46ha1p_81674 94 1.81 74% 29 of 39 87% 27 of 31 ha1p_86355 95 1.95 39% 17of 44 88% 36 of 41 ha1p_98491 96 2.13 38% 18 of 47 94% 44 of 47ha1p_99426 97 1.02 83% 39 of 47 92% 44 of 48 Threshold: Average dCtvalue established by ROC curve analysis as optimal threshold fordistinguishing tumor and adjacent normal tissues. Sensitivity: % ofpositive (i.e., methylation score above Threshold for gain ofmethylation markers or below Threshold for loss of methylation markers)tumors. Pos. of Total: Number of positive tumors relative to the totalnumber of tumors analyzed. Specificity: % of negative (i.e., methylationscore below Threshold for gain of methylation markers or above Thresholdfor loss of methylation markers) adjacent normal samples. Neg. of Total:Number of negative adjacent normal samples relative to the total numberof adjacent normal samples analyzed.

TABLE 22 Sensitivity and Specificity of differentially methylated lociin lung tumors relative to histologically normal lung tissue. LocusThresh- Sensi- Pos. of Spec- Neg. of Feature No. old tivity Totalificity Total ha1g_00681 49 — — — — — ha1g_01966 50 1.3  89% 24 of 2795% 20 of 21 ha1g_02153 51 0.66 81% 22 of 27 91% 20 of 22 ha1g_02319 520.57 83% 24 of 29 95% 21 of 22 ha1g_02335 53 — — — — — ha1p16_00182 541.05 85% 23 of 27 86% 19 of 22 ha1p16_00185 55 1.13 76% 19 of 25 100% 22 of 22 ha1p16_00193 56 1.83 81% 22 of 27 90% 19 of 21 ha1p16_00259 572.31 77% 20 of 26 100%  21 of 21 ha1p_02799 58 1.91 50% 14 of 28 100% 21 of 21 ha1p_03567 59 1.34 81% 22 of 27 91% 20 of 22 ha1p_03671 60 1.8646% 13 of 28 90% 19 of 21 ha1p_05803 61 — — — — — ha1p_07131 62 3.89 82%23 of 28 100%  22 of 22 ha1p_07989 63 3.1  86% 24 of 28 95% 21 of 22ha1p_08588 64 4.35 86% 24 of 28 95% 20 of 21 ha1p_09700 65 1.01 64% 18of 28 91% 20 of 22 ha1p_104458 66 — — — — — ha1p_105287 67 1.96 79% 23of 29 95% 20 of 21 ha1p_10702 68 1.16 41%  9 of 22 100%  19 of 19ha1p_108469 69 0.99 78% 21 of 27 95% 20 of 21 ha1p_108849 70 — — — — —ha1p_11016 71 — — — — — ha1p_11023 72 — — — — — ha1p_12974 73 0.61 21% 6 of 28 95% 21 of 22 ha1p_16027 74 0.66 86% 25 of 29 86% 19 of 22ha1p_16066 75 0.79 72% 21 of 29 100%  20 of 20 ha1p_18911 76 2.69 66% 19of 29 100%  22 of 22 ha1p_19254 77 — — — — — ha1p_19853 78 0.75 86% 24of 28 91% 20 of 22 ha1p_22257 79 1.56 54% 15 of 28 91% 20 of 22ha1p_22519 80 1.68 89% 24 of 27 95% 21 of 22 ha1p_31800 81 2.59 76% 22of 29 86% 19 of 22 ha1p_33290 82 1.94 86% 25 of 29 95% 21 of 22ha1p_37635 83 — — — — — ha1p_39189 84 0.89 86% 24 of 28 86% 19 of 22ha1p_39511 85 — — — — — ha1p_39752 86 1.96 56% 15 of 27 82% 18 of 22ha1p_60945 87 — — — — — ha1p_62183 88 3.55 81% 21 of 26 100%  22 of 22ha1p_69418 89 2.67 85% 23 of 27 100%  21 of 21 ha1p_71224 90 2.01 84% 21of 25 79% 15 of 19 ha1p_74221 91 — — — — — ha1p_76289 92 1.13 80% 20 of25 83% 15 of 18 ha1p_81050 93 6   88% 15 of 17 95% 21 of 22 ha1p_8167494 2.4  65% 13 of 20 100%  18 of 18 ha1p_86355 95 1.7  44% 11 of 25100%  22 of 22 ha1p_98491 96 2.14 43% 12 of 28 95% 21 of 22 ha1p_9942697 1.02 86% 24 of 28 100%  22 of 22 Threshold: Average dCt valueestablished by ROC curve analysis as optimal threshold fordistinguishing tumor and adjacent normal tissues. Sensitivity: % ofpositive (i.e., methylation score above Threshold for gain ofmethylation markers or below Threshold for loss of methylation markers)tumors. Pos. of Total: Number of positive tumors relative to the totalnumber of tumors analyzed. Specificity: % of negative (i.e., methylationscore below Threshold for gain of methylation markers or above Thresholdfor loss of methylation markers) benign normal samples. Neg. of Total:Number of negative benign normal samples relative to the total number ofadjacent normal samples analyzed.

TABLE 23 Frequency of methylation of each locus in melanoma tumors.Locus Feature Name Number Threshold Sensitivity Pos. of TotalCHR01P001976799 1 1.0 100.00%    7 of 7 CHR01P026794862 2 1.0 33.33%   2of 6 CHR01P043164342 3 1.0 100.00%   7 of 7 CHR01P063154999 4 1.0100.00%    7 of 7 CHR01P204123050 5 1.0 85.71%   6 of 7 CHR01P2069051106 1.0 100.00%    7 of 7 CHR01P225608458 7 1.0 85.71%   6 of 7CHR02P005061785 8 1.0 100.00%    7 of 7 CHR02P042255672 9 1.0 100.00%   7 of 7 CHR02P223364582 10 1.0 42.86%   3 of 7 CHR03P027740753 11 1.0100.00%    7 of 7 CHR03P052525960 12 1.0 100.00%    7 of 7CHR03P069745999 13 1.0 100.00%    7 of 7 CHR05P059799713 14 1.0100.00%    7 of 7 CHR05P059799813 15 1.0 100.00%    7 of 7CHR05P177842690 16 1.0 100.00%    7 of 7 CHR06P010694062 17 1.0 85.71%  6 of 7 CHR06P026333318 18 1.0 85.71%   6 of 7 CHR08P102460854 19 1.085.71%   6 of 7 CHR08P102461254 20 1.0 100.00%    7 of 7 CHR08P10246155421 1.0 85.71%   6 of 7 CHR09P000107988 22 1.0 85.71%   6 of 7CHR09P021958839 23 1.0 85.71%   6 of 7 CHR09P131048752 24 1.0 57.14%   4of 7 CHR10P118975684 25 1.0 85.71%   6 of 7 CHR11P021861414 26 1.0100.00%    7 of 7 CHR12P004359362 27 1.0 71.43%   5 of 7 CHR12P01600123128 1.0 85.71%   6 of 7 CHR14P018893344 29 1.0 85.71%   6 of 7CHR14P093230340 30 1.0 83.33%   5 of 6 CHR16P000373719 31 1.0 50.00%   2of 4 CHR16P066389027 32 1.0 71.43%   5 of 7 CHR16P083319654 33 1.071.43%   5 of 7 CHR18P019705147 34 1.0 100.00%    7 of 7 CHR19P01862240835 1.0 66.67%   4 of 6 CHR19P051892823 36 1.0 25.00%   1 of 4CHRXP013196410 37 1.0 100.00%    5 of 5 CHRXP013196870 38 1.0 85.71%   6of 7 ha1p16_00179_l50 39 1.0 85.71%   6 of 7 ha1p16_00182_l50 40 1.042.86%   3 of 7 ha1p16_00257_l50 41 1.0 57.14%   4 of 7 ha1p_12601_l5042 1.0 71.43%   5 of 7 ha1p_17147_l50 43 1.0 100.00%    7 of 7ha1p_42350_l50 44 1.0 100.00%    4 of 4 ha1p_44897_l50 45 1.0 100.00%   7 of 7 ha1p_61253_l50 46 1.0 57.14%   4 of 7 CHR01P001005050 47 1.0100.00%    4 of 4 CHR16P001157479 48 1.0 100.00%    1 of 1 ha1g_00681 491.0 71% 5 of 7 ha1g_01966 50 1.0 86% 6 of 7 ha1g_02153 51 1.0 86% 6 of 7ha1g_02319 52 1.0 57% 4 of 7 ha1g_02335 53 1.0 86% 6 of 7 ha1p16_0018254 1.0 43% 3 of 7 ha1p16_00185 55 1.0 71% 5 of 7 ha1p16_00193 56 1.0100%  6 of 6 ha1p16_00259 57 1.0 100%  7 of 7 ha1p_02799 58 1.0 100%  7of 7 ha1p_03567 59 1.0 57% 4 of 7 ha1p_03671 60 1.0  0% 0 of 6ha1p_05803 61 1.0 57% 4 of 7 ha1p_07131 62 1.0 86% 6 of 7 ha1p_07989 631.0 80% 4 of 5 ha1p_08588 64 1.0 86% 6 of 7 ha1p_09700 65 1.0 50% 2 of 4ha1p_104458 66 1.0 86% 6 of 7 ha1p_105287 67 1.0 100%  7 of 7 ha1p_1070268 1.0 71% 5 of 7 ha1p_108469 69 1.0 86% 6 of 7 ha1p_108849 70 1.0 100% 7 of 7 ha1p_11016 71 1.0 100%  7 of 7 ha1p_11023 72 1.0 86% 6 of 7ha1p_12974 73 1.0 14% 1 of 7 ha1p_16027 74 1.0 100%  7 of 7 ha1p_1606675 1.0 86% 6 of 7 ha1p_18911 76 1.0 86% 6 of 7 ha1p_19254 77 1.0 100%  7of 7 ha1p_19853 78 1.0 29% 2 of 7 ha1p_22257 79 1.0 100%  7 of 7ha1p_22519 80 1.0 86% 6 of 7 ha1p_31800 81 1.0 100%  7 of 7 ha1p_3329082 1.0 86% 6 of 7 ha1p_37635 83 1.0 100%  7 of 7 ha1p_39189 84 1.0 86% 6of 7 ha1p_39511 85 1.0 43% 3 of 7 ha1p_39752 86 1.0 100%  7 of 7ha1p_60945 87 1.0 86% 6 of 7 ha1p_62183 88 1.0 100%  7 of 7 ha1p_6941889 1.0 71% 5 of 7 ha1p_71224 90 1.0 57% 4 of 7 ha1p_74221 91 1.0 86% 6of 7 ha1p_76289 92 1.0 57% 4 of 7 ha1p_81050 93 1.0 86% 6 of 7ha1p_81674 94 1.0 100%  7 of 7 ha1p_86355 95 1.0 100%  7 of 7 ha1p_9849196 1.0 86% 6 of 7 ha1p_99426 97 1.0 71% 5 of 7 Sensitivity: % ofpositive (i.e., methylation score above 1.0) tumors. Pos. of Total:Number of positive tumors relative to the total number of tumorsanalyzed. Note that adjacent histology normal or normal skin sampleswere not available for analysis. Threshold for a positive methylationscore was set at an average dCt of 1.0.

TABLE 24 Sensitivity and Specificity of differentially methylated lociin ovarian tumors relative to histologically normal ovarian tissue.Locus Pos. of Neg. of Feature Name Number Threshold Sensitivity TotalSpecificity Total CHR01P001976799 1 1.405 91.18%   31 of 34 96.97%   32of 33 CHR01P026794862 2 1.32 25.00%    7 of 28 87.88%   29 of 33CHR01P043164342 3 5.35 91.18%   31 of 34 97.14%   34 of 35CHR01P063154999 4 0.8 85.29%   29 of 34 94.12%   32 of 34CHR01P204123050 5 1.165 72.73%   24 of 33 73.53%   25 of 34CHR01P206905110 6 5.6 88.24%   30 of 34 97.14%   34 of 35CHR01P225608458 7 1.51 73.53%   25 of 34 94.29%   33 of 35CHR02P005061785 8 1.565 93.94%   31 of 33 97.14%   34 of 35CHR02P042255672 9 0.835 85.29%   29 of 34 97.14%   34 of 35CHR02P223364582 10 1.485 85.29%   29 of 34 94.29%   33 of 35CHR03P027740753 11 0.69 85.29%   29 of 34 96.97%   32 of 33CHR03P052525960 12 1.985 55.88%   19 of 34 97.06%   33 of 34CHR03P069745999 13 4.645 73.53%   25 of 34 94.29%   33 of 35CHR05P059799713 14 1.735 64.71%   22 of 34 90.91%   30 of 33CHR05P059799813 15 1.78 64.71%   22 of 34 88.24%   30 of 34CHR05P177842690 16 1.545 85.29%   29 of 34 85.71%   30 of 35CHR06P010694062 17 1.235 85.29%   29 of 34 85.71%   30 of 35CHR06P026333318 18 1.705 94.12%   32 of 34 94.29%   33 of 35CHR08P102460854 19 1.045 81.82%   27 of 33 94.29%   33 of 35CHR08P102461254 20 1.835 87.88%   29 of 33 94.29%   33 of 35CHR08P102461554 21 1.57 84.85%   28 of 33 94.29%   33 of 35CHR09P000107988 22 0.75 88.24%   30 of 34 82.86%   29 of 35CHR09P021958839 23 1.575 79.41%   27 of 34 96.97%   32 of 33CHR09P131048752 24 1.055 91.18%   31 of 34 94.29%   33 of 35CHR10P118975684 25 2.51 61.76%   21 of 34 91.43%   32 of 35CHR11P021861414 26 4.195 47.06%   16 of 34 97.14%   34 of 35CHR12P004359362 27 2.52 67.65%   23 of 34 94.12%   32 of 34CHR12P016001231 28 1.375 71.88%   23 of 32 84.85%   28 of 33CHR14P018893344 29 1.185 82.35%   28 of 34 100.00%    34 of 34CHR14P093230340 30 1.695 91.18%   31 of 34 97.14%   34 of 35CHR16P000373719 31 2.57 84.21%   16 of 19 82.14%   23 of 28CHR16P066389027 32 0.595 64.71%   22 of 34 88.57%   31 of 35CHR16P083319654 33 1.355 67.65%   23 of 34 88.57%   31 of 35CHR18P019705147 34 6 88.24%   30 of 34 97.06%   33 of 34 CHR19P01862240835 0.87 91.18%   31 of 34 97.06%   33 of 34 CHR19P051892823 36 1.0364.29%    9 of 14 86.67%   13 of 15 CHRXP013196410 37 0.905 96.97%   32of 33 84.38%   27 of 32 CHRXP013196870 38 0.795 100.00%    34 of 3475.76%   25 of 33 ha1p16_00179_l50 39 1.085 88.24%   30 of 34 94.29%  33 of 35 ha1p16_00182_l50 40 1.315 79.41%   27 of 34 100.00%    35 of 35ha1p16_00257_l50 41 1.07 86.21%   25 of 29 90.00%   27 of 30ha1p_12601_l50 42 2.915 94.12%   32 of 34 94.12%   32 of 34ha1p_17147_l50 43 3.72 93.94%   31 of 33 94.12%   32 of 34ha1p_42350_l50 44 0.785 62.07%   18 of 29 96.67%   29 of 30ha1p_44897_l50 45 2.84 88.24%   30 of 34 96.97%   32 of 33ha1p_61253_l50 46 0.905 79.31%   23 of 29 96.30%   26 of 27CHR01P001005050 47 5.195 100.00%    28 of 28 87.50%   7 of 8CHR16P001157479 48 3 84.21%   16 of 19 91.67%   11 of 12 ha1g_00681 490.75 78% 14 of 18 100%  18 of 18 ha1g_01966 50 1.54 89% 16 of 18 100% 18 of 18 ha1g_02153 51 3.62 39%  7 of 18 94% 17 of 18 ha1g_02319 52 0.5778% 14 of 18 94% 17 of 18 ha1g_02335 53 4.77 61% 11 of 18 83% 15 of 18ha1p16_00182 54 0.87 83% 15 of 18 94% 17 of 18 ha1p16_00185 55 1.39 88%15 of 17 89% 16 of 18 ha1p16_00193 56 1.96 89% 16 of 18 100%  18 of 18ha1p16_00259 57 1.91 83% 15 of 18 56% 10 of 18 ha1p_02799 58 5.54 61% 11of 18 78% 14 of 18 ha1p_03567 59 1.21 56% 10 of 18 67% 12 of 18ha1p_03671 60 0.65 78% 14 of 18 56% 10 of 18 ha1p_05803 61 1.96 78% 14of 18 100%  18 of 18 ha1p_07131 62 5.03 76% 13 of 17 89% 16 of 18ha1p_07989 63 2.75 39%  7 of 18 100%  16 of 16 ha1p_08588 64 6 61% 11 of18 100%  18 of 18 ha1p_09700 65 0.55 25%  4 of 16 88% 14 of 16ha1p_104458 66 5.08 78% 14 of 18 100%  18 of 18 ha1p_105287 67 5.01 94%17 of 18 94% 17 of 18 ha1p_10702 68 0.54 44%  8 of 18 100%  18 of 18ha1p_108469 69 1.42 78% 14 of 18 78% 14 of 18 ha1p_108849 70 3.1 56% 10of 18 100%  18 of 18 ha1p_11016 71 4.39 94% 17 of 18 78% 14 of 18ha1p_11023 72 2.83 39%  7 of 18 100%  18 of 18 ha1p_12974 73 0.55 44%  8of 18 89% 16 of 18 ha1p_16027 74 1.02 83% 15 of 18 100%  17 of 17ha1p_16066 75 2.47 78% 14 of 18 56% 10 of 18 ha1p_18911 76 3.16 78% 14of 18 100%  18 of 18 ha1p_19254 77 3.77 67% 12 of 18 100%  18 of 18ha1p_19853 78 0.61 83% 15 of 18 89% 16 of 18 ha1p_22257 79 1.7 72% 13 of18 100%  18 of 18 ha1p_22519 80 2.05 78% 14 of 18 100%  18 of 18ha1p_31800 81 2.82 82% 14 of 17 83% 15 of 18 ha1p_33290 82 1.52 72% 13of 18 94% 17 of 18 ha1p_37635 83 6  6%  1 of 18 100%  17 of 17ha1p_39189 84 0.86 88% 14 of 16 76% 13 of 17 ha1p_39511 85 3.87 44%  8of 18 83% 15 of 18 ha1p_39752 86 1.73 78% 14 of 18 100%  18 of 18ha1p_60945 87 0.93 61% 11 of 18 72% 13 of 18 ha1p_62183 88 4.12 33%  6of 18 100%  18 of 18 ha1p_69418 89 2.67 61% 11 of 18 94% 17 of 18ha1p_71224 90 1.21 89% 16 of 18 89% 16 of 18 ha1p_74221 91 1.85 59% 10of 17 88% 15 of 17 ha1p_76289 92 0.61 79% 11 of 14 88% 14 of 16ha1p_81050 93 5.34 78% 14 of 18 100%  18 of 18 ha1p_81674 94 0.98 89% 16of 18 89% 16 of 18 ha1p_86355 95 1.6 78% 14 of 18 100%  18 of 18ha1p_98491 96 4.57 78% 14 of 18 100%  18 of 18 ha1p_99426 97 0.53 83% 15of 18 89% 16 of 18 Threshold: Average dCt value established by ROC curveanalysis as optimal threshold for distinguishing tumor and adjacentnormal tissues. Sensitivity: % of positive (i.e. methylation score aboveThreshold for gain of methylation markers or below Threshold for loss ofmethylation markers) tumors. Pos. of Total: Number of positive tumorsrelative to the total number of tumors analyzed. Specificity: % ofnegative (i.e. methylation score below Threshold for gain of methylationmarkers or above Threshold for loss of methylation markers) adjacentnormal samples. Neg. of Total: Number of negative adjacent normalsamples relative to the total number of adjacent normal samplesanalyzed.

TABLE 25 Sensitivity and Specificity of differentially methylated lociin prostate tumors relative to adjacent histological normal prostatetissue. Locus Pos. of Neg. of Feature Name Number Threshold SensitivityTotal Specificity Total CHR01P001976799 1 4.025 100.00%    9 of 933.33%   3 of 9 CHR01P026794862 2 0.505 0.00%   0 of 3 80.00%   4 of 5CHR01P043164342 3 1.755 66.67%   6 of 9 77.78%   7 of 9 CHR01P0631549994 1.535 50.00%   4 of 8 100.00%    9 of 9 CHR01P204123050 5 1.8871.43%   5 of 7 87.50%   7 of 8 CHR01P206905110 6 2.93 66.67%   6 of 9100.00%    8 of 8 CHR01P225608458 7 1.785 88.89%   8 of 9 77.78%   7 of9 CHR02P005061785 8 3.505 88.89%   8 of 9 77.78%   7 of 9CHR02P042255672 9 1.98 100.00%    9 of 9 77.78%   7 of 9 CHR02P22336458210 1.94 66.67%   6 of 9 100.00%    9 of 9 CHR03P027740753 11 1.377.78%   7 of 9 100.00%    9 of 9 CHR03P052525960 12 2.135 77.78%   7 of9 77.78%   7 of 9 CHR03P069745999 13 1.585 75.00%   6 of 8 66.67%   6 of9 CHR05P059799713 14 0.685 55.56%   5 of 9 77.78%   7 of 9CHR05P059799813 15 0.69 55.56%   5 of 9 66.67%   6 of 9 CHR05P17784269016 2.055 55.56%   5 of 9 77.78%   7 of 9 CHR06P010694062 17 2.3688.89%   8 of 9 88.89%   8 of 9 CHR06P026333318 18 1.825 87.50%   7 of 8100.00%    9 of 9 CHR08P102460854 19 0.82 100.00%    9 of 9 66.67%   6of 9 CHR08P102461254 20 0.82 88.89%   8 of 9 66.67%   6 of 9CHR08P102461554 21 0.925 100.00%    9 of 9 62.50%   5 of 8CHR09P000107988 22 1.355 88.89%   8 of 9 77.78%   7 of 9 CHR09P02195883923 1.565 77.78%   7 of 9 100.00%    9 of 9 CHR09P131048752 24 2.1977.78%   7 of 9 100.00%    8 of 8 CHR10P118975684 25 1.665 55.56%   5 of9 87.50%   7 of 8 CHR11P021861414 26 4.785 87.50%   7 of 8 88.89%   8 of9 CHR12P004359362 27 2.895 66.67%   6 of 9 100.00%    9 of 9CHR12P016001231 28 1.365 77.78%   7 of 9 66.67%   6 of 9 CHR14P01889334429 1.48 66.67%   6 of 9 100.00%    9 of 9 CHR14P093230340 30 1.9777.78%   7 of 9 88.89%   8 of 9 CHR16P000373719 31 0.81 50.00%   3 of 6100.00%    3 of 3 CHR16P066389027 32 1.31 55.56%   5 of 9 77.78%   7 of9 CHR16P083319654 33 1.95 66.67%   6 of 9 88.89%   8 of 9CHR18P019705147 34 2.89 85.71%   6 of 7 88.89%   8 of 9 CHR19P01862240835 1.65 88.89%   8 of 9 77.78%   7 of 9 CHR19P051892823 36 1.175100.00%    3 of 3 66.67%   2 of 3 CHRXP013196410 37 3.205 100.00%    9of 9 77.78%   7 of 9 CHRXP013196870 38 4.015 77.78%   7 of 9 87.50%   7of 8 ha1p16_00179_l50 39 1.55 66.67%   6 of 9 100.00%    8 of 8ha1p16_00182_l50 40 1.32 55.56%   5 of 9 100.00%    9 of 9ha1p16_00257_l50 41 1.52 77.78%   7 of 9 100.00%    9 of 9ha1p_12601_l50 42 1.01 100.00%    9 of 9 77.78%   7 of 9 ha1p_17147_l5043 1.425 88.89%   8 of 9 66.67%   6 of 9 ha1p_42350_l50 44 3.88 66.67%  6 of 9 71.43%   5 of 7 ha1p_44897_l50 45 3.845 55.56%   5 of 9100.00%    9 of 9 ha1p_61253_l50 46 1.4 62.50%   5 of 8 85.71%   6 of 7CHR01P001005050 47 0.69 75.00%   6 of 8 55.56%   5 of 9 CHR16P00115747948 — — — — — ha1g_00681 49 0.76 89% 8 of 9 89% 8 of 9 ha1g_01966 50 2.5489% 8 of 9 67% 6 of 9 ha1g_02153 51 1.04 56% 5 of 9 78% 7 of 9ha1g_02319 52 0.86 100%  9 of 9 56% 5 of 9 ha1g_02335 53 3.58 56% 5 of 967% 6 of 9 ha1p16_00182 54 1.14 75% 6 of 8 78% 7 of 9 ha1p16_00185 551.04 78% 7 of 9 78% 7 of 9 ha1p16_00193 56 1.6 78% 7 of 9 78% 7 of 9ha1p16_00259 57 2.02 78% 7 of 9 78% 7 of 9 ha1p_02799 58 4.92 22% 2 of 9100%  9 of 9 ha1p_03567 59 1.8 44% 4 of 9 78% 7 of 9 ha1p_03671 60 1.2689% 8 of 9 78% 7 of 9 ha1p_05803 61 1.67 89% 8 of 9 89% 8 of 9ha1p_07131 62 6 67% 6 of 9 78% 7 of 9 ha1p_07989 63 2.88 75% 6 of 8 89%8 of 9 ha1p_08588 64 5.59 44% 4 of 9 78% 7 of 9 ha1p_09700 65 0.84 86% 6of 7 100%  9 of 9 ha1p_104458 66 3.59 100%  9 of 9 67% 6 of 9ha1p_105287 67 1.24 67% 6 of 9 67% 6 of 9 ha1p_10702 68 2.81 78% 7 of 978% 7 of 9 ha1p_108469 69 1.13 100%  9 of 9 78% 7 of 9 ha1p_108849 702.24 56% 5 of 9 67% 6 of 9 ha1p_11016 71 3.63 100%  9 of 9 78% 7 of 9ha1p_11023 72 1.8 78% 7 of 9 67% 6 of 9 ha1p_12974 73 1.05 67% 6 of 9100%  9 of 9 ha1p_16027 74 1.24 89% 8 of 9 50% 4 of 8 ha1p_16066 75 2.53100%  9 of 9 44% 4 of 9 ha1p_18911 76 2.49 100%  9 of 9 75% 6 of 8ha1p_19254 77 4.59 50% 4 of 8 78% 7 of 9 ha1p_19853 78 0.97 88% 7 of 888% 7 of 8 ha1p_22257 79 2.7 78% 7 of 9 67% 6 of 9 ha1p_22519 80 1.51100%  9 of 9 78% 7 of 9 ha1p_31800 81 2.51 100%  9 of 9 56% 5 of 9ha1p_33290 82 1.77 78% 7 of 9 78% 7 of 9 ha1p_37635 83 6 100%  9 of 9 0% 0 of 9 ha1p_39189 84 1.52 89% 8 of 9 78% 7 of 9 ha1p_39511 85 3.5989% 8 of 9 44% 4 of 9 ha1p_39752 86 1.82 56% 5 of 9 78% 7 of 9ha1p_60945 87 1.41 78% 7 of 9 56% 5 of 9 ha1p_62183 88 4.21 67% 6 of 978% 7 of 9 ha1p_69418 89 3.72 63% 5 of 8 78% 7 of 9 ha1p_71224 90 1.9256% 5 of 9 78% 7 of 9 ha1p_74221 91 1.71 78% 7 of 9 44% 4 of 9ha1p_76289 92 0.6 100%  9 of 9 56% 5 of 9 ha1p_81050 93 6 44% 4 of 9 78%7 of 9 ha1p_81674 94 1.2 100%  8 of 8 63% 5 of 8 ha1p_86355 95 1.54100%  8 of 8 56% 5 of 9 ha1p_98491 96 2.91 78% 7 of 9 56% 5 of 9ha1p_99426 97 0.77 78% 7 of 9 78% 7 of 9 Threshold: Average dCt valueestablished by ROC curve analysis as optimal threshold fordistinguishing tumor and adjacent normal tissues. Sensitivity: % ofpositive (i.e., methylation score above Threshold for gain ofmethylation markers or below Threshold for loss of methylation markers)tumors. Pos. of Total: Number of positive tumors relative to the totalnumber of tumors analyzed. Specificity: % of negative (i.e., methylationscore below Threshold for gain of methylation markers or above Thresholdfor loss of methylation markers) adjacent normal samples. Neg. of Total:Number of negative adjacent normal samples relative to the total numberof adjacent normal samples analyzed.

TABLE 26 Sensitivity and Specificity of differentially methylated lociin renal tumors relative to adjacent histological normal renal tissue.Locus Pos. of Neg. of Feature Name Number Threshold Sensitivity TotalSpecificity Total CHR01P001976799 1 4.745 66.67%   6 of 9  90.00%   9 of10 CHR01P026794862 2 0.525 75.00%   6 of 8  80.00%   4 of 5 CHR01P043164342 3 1.555 100.00%    10 of 10  80.00%   8 of 10CHR01P063154999 4 1.28 90.00%   9 of 10 80.00%   8 of 10 CHR01P2041230505 2.105 77.78%   7 of 9  55.56%   5 of 9  CHR01P206905110 6 4.28560.00%   6 of 10 100.00%    10 of 10  CHR01P225608458 7 1.56 80.00%   8of 10 90.00%   9 of 10 CHR02P005061785 8 4.32 80.00%   8 of 10 40.00%  4 of 10 CHR02P042255672 9 2.145 60.00%   6 of 10 90.00%   9 of 10CHR02P223364582 10 1.89 66.67%   6 of 9  90.00%   9 of 10CHR03P027740753 11 1.23 90.00%   9 of 10 90.00%   9 of 10CHR03P052525960 12 2.69 80.00%   8 of 10 90.00%   9 of 10CHR03P069745999 13 1.615 100.00%    10 of 10  90.00%   9 of 10CHR05P059799713 14 3.425 80.00%   8 of 10 100.00%    10 of 10 CHR05P059799813 15 3.395 80.00%   8 of 10 100.00%    10 of 10 CHR05P177842690 16 1.685 100.00%    10 of 10  40.00%   4 of 10CHR06P010694062 17 2.27 80.00%   8 of 10 70.00%   7 of 10CHR06P026333318 18 2.18 100.00%    10 of 10  100.00%    10 of 10 CHR08P102460854 19 1.06 90.00%   9 of 10 90.00%   9 of 10CHR08P102461254 20 1.255 90.00%   9 of 10 80.00%   8 of 10CHR08P102461554 21 1.375 90.00%   9 of 10 90.00%   9 of 10CHR09P000107988 22 1.33 100.00%    10 of 10  50.00%   5 of 10CHR09P021958839 23 1.405 90.00%   9 of 10 90.00%   9 of 10CHR09P131048752 24 2.51 90.00%   9 of 10 90.00%   9 of 10CHR10P118975684 25 1.265 60.00%   6 of 10 90.00%   9 of 10CHR11P021861414 26 4.58 100.00%    10 of 10  100.00%    10 of 10 CHR12P004359362 27 2.055 40.00%   4 of 10 100.00%    10 of 10 CHR12P016001231 28 0.72 100.00%    9 of 9  60.00%   6 of 10CHR14P018893344 29 1.52 90.00%   9 of 10 88.89%   8 of 9 CHR14P093230340 30 1.85 70.00%   7 of 10 88.89%   8 of 9 CHR16P000373719 31 1.585 88.89%   8 of 9  50.00%   4 of 8 CHR16P066389027 32 1.945 100.00%    10 of 10  77.78%   7 of 9 CHR16P083319654 33 2.525 90.00%   9 of 10 62.50%   5 of 8 CHR18P019705147 34 4.07 70.00%   7 of 10 100.00%    9 of 9 CHR19P018622408 35 1.7 90.00%   9 of 10 40.00%   4 of 10 CHR19P05189282336 2.7 57.14%   4 of 7  100.00%    6 of 6  CHRXP013196410 37 0.68100.00%    10 of 10  66.67%   6 of 9  CHRXP013196870 38 0.905 80.00%   8of 10 80.00%   8 of 10 ha1p16_00179_l50 39 1.375 90.00%   9 of 1080.00%   8 of 10 ha1p16_00182_l50 40 0.92 88.89%   8 of 9  80.00%   8 of10 ha1p16_00257_l50 41 0.95 100.00%    10 of 10  88.89%   8 of 9 ha1p_12601_l50 42 0.745 100.00%    10 of 10  77.78%   7 of 9 ha1p_17147_l50 43 1.74 100.00%    9 of 9  90.00%   9 of 10ha1p_42350_l50 44 1.54 80.00%   8 of 10 88.89%   8 of 9  ha1p_44897_l5045 4.92 40.00%   4 of 10 88.89%   8 of 9  ha1p_61253_l50 46 1.9680.00%   8 of 10 70.00%   7 of 10 CHR01P001005050 47 2.8 100.00%    8 of8  80.00%   8 of 10 CHR16P001157479 48 4.66 100.00%    2 of 2 100.00%    1 of 1  ha1g_00681 49 1.89 60% 6 of 10 78% 7 of 9  ha1g_0196650 1.21 100%  10 of 10  88% 7 of 8  ha1g_02153 51 0.92 70% 7 of 10 90% 9of 10 ha1g_02319 52 1.04 90% 9 of 10 40% 4 of 10 ha1g_02335 53 2.11 90%9 of 10 90% 9 of 10 ha1p16_00182 54 0.85 89% 8 of 9  90% 9 of 10ha1p16_00185 55 0.53 100%  9 of 9  70% 7 of 10 ha1p16_00193 56 1.54100%  7 of 7  80% 8 of 10 ha1p16_00259 57 1.79 100%  10 of 10  90% 9 of10 ha1p_02799 58 3.52 60% 6 of 10 100%  10 of 10  ha1p_03567 59 1.82 50%5 of 10 80% 8 of 10 ha1p_03671 60 0.79 60% 6 of 10 80% 8 of 10ha1p_05803 61 1.82 70% 7 of 10 70% 7 of 10 ha1p_07131 62 6 80% 8 of 10100%  10 of 10  ha1p_07989 63 3.3 90% 9 of 10 100%  10 of 10  ha1p_0858864 5.67 60% 6 of 10 100%  10 of 10  ha1p_09700 65 0.98 13% 1 of 8  100% 8 of 8  ha1p_104458 66 4.06 90% 9 of 10 90% 9 of 10 ha1p_105287 67 4.48100%  10 of 10  100%  10 of 10  ha1p_10702 68 0.95 80% 8 of 10 40% 4 of10 ha1p_108469 69 1.63 70% 7 of 10 40% 4 of 10 ha1p_108849 70 2.75 100% 10 of 10  70% 7 of 10 ha1p_11016 71 2.71 60% 6 of 10 90% 9 of 10ha1p_11023 72 1.67 100%  10 of 10  90% 9 of 10 ha1p_12974 73 0.57 30% 3of 10 80% 8 of 10 ha1p_16027 74 1.99 50% 5 of 10 100%  10 of 10 ha1p_16066 75 1.52 90% 9 of 10 80% 8 of 10 ha1p_18911 76 2.16 80% 8 of10 100%  10 of 10  ha1p_19254 77 4.53 90% 9 of 10 90% 9 of 10 ha1p_1985378 0.5 100%  10 of 10  90% 9 of 10 ha1p_22257 79 2.11 50% 5 of 10 90% 9of 10 ha1p_22519 80 1.47 80% 8 of 10 90% 9 of 10 ha1p_31800 81 2.46 40%4 of 10 90% 9 of 10 ha1p_33290 82 1.09 89% 8 of 9 80% 8 of 10 ha1p_3763583 6 100%  10 of 10   0% 0 of 10 ha1p_39189 84 0.78 90% 9 of 10 80% 8 of10 ha1p_39511 85 1.87 78% 7 of 9  78% 7 of 9  ha1p_39752 86 1.35 89% 8of 9  89% 8 of 9  ha1p_60945 87 1.97 60% 6 of 10 100%  10 of 10 ha1p_62183 88 5.06 80% 8 of 10 100%  10 of 10  ha1p_69418 89 2.15 70% 7of 10 90% 9 of 10 ha1p_71224 90 1.69 70% 7 of 10 90% 9 of 10 ha1p_7422191 1.22 50% 5 of 10 90% 9 of 10 ha1p_76289 92 0.97 90% 9 of 10 80% 8 of10 ha1p_81050 93 6 80% 8 of 10 100%  10 of 10  ha1p_81674 94 1.24 100% 10 of 10  30% 3 of 10 ha1p_86355 95 1.17 78% 7 of 9  50% 5 of 10ha1p_98491 96 4.12 80% 8 of 10 80% 8 of 10 ha1p_99426 97 0.65 90% 9 of10 90% 9 of 10 Threshold: Average dCt value established by ROC curveanalysis as optimal threshold for distinguishing tumor and adjacentnormal tissues. Sensitivity: % of positive (i.e., methylation scoreabove Threshold for gain of methylation markers or below Threshold forloss of methylation markers) tumors. Pos. of Total: Number of positivetumors relative to the total number of tumors analyzed. Specificity: %of negative (i.e., methylation score below Threshold for gain ofmethylation markers or above Threshold for loss of methylation markers)adjacent normal samples. Neg. of Total: Number of negative adjacentnormal samples relative to the total number of adjacent normal samplesanalyzed.

TABLE 27 Sensitivity and Specificity of differentially methylated lociin thyroid tumors relative to adjacent histological normal thyroidtissue. Locus Pos. of Neg. of Feature Name Number Threshold SensitivityTotal Specificity Total CHR01P001976799 1 6 100.00% 10 of 10  0.00% 0 of10 CHR01P026794862 2 1.095 66.67% 2 of 3  100.00% 1 of 1 CHR01P043164342 3 2.01 100.00% 10 of 10  10.00% 1 of 10 CHR01P0631549994 1.205 60.00% 6 of 10 100.00% 10 of 10  CHR01P204123050 5 2.795 85.71%6 of 7  50.00% 3 of 6  CHR01P206905110 6 1.195 60.00% 6 of 10 100.00% 10of 10  CHR01P225608458 7 0.62 90.00% 9 of 10 70.00% 7 of 10CHR02P005061785 8 4.49 70.00% 7 of 10 90.00% 9 of 10 CHR02P042255672 93.895 100.00% 10 of 10  60.00% 6 of 10 CHR02P223364582 10 2.015 90.00% 9of 10 80.00% 8 of 10 CHR03P027740753 11 0.77 100.00% 10 of 10  90.00% 9of 10 CHR03P052525960 12 3.265 90.00% 9 of 10 80.00% 8 of 10CHR03P069745999 13 1.06 80.00% 8 of 10 50.00% 5 of 10 CHR05P059799713 140.55 80.00% 8 of 10 40.00% 4 of 10 CHR05P059799813 15 0.615 70.00% 7 of10 44.44% 4 of 9  CHR05P177842690 16 0.935 80.00% 8 of 10 40.00% 4 of 10CHR06P010694062 17 3.58 50.00% 5 of 10 80.00% 8 of 10 CHR06P026333318 182 90.00% 9 of 10 90.00% 9 of 10 CHR08P102460854 19 0.505 10.00% 1 of 1090.00% 9 of 10 CHR08P102461254 20 0.555 40.00% 4 of 10 80.00% 8 of 10CHR08P102461554 21 0.545 40.00% 4 of 10 70.00% 7 of 10 CHR09P00010798822 1.255 60.00% 6 of 10 80.00% 8 of 10 CHR09P021958839 23 1.03 100.00%10 of 10  80.00% 8 of 10 CHR09P131048752 24 3.27 80.00% 8 of 10 88.89% 8of 9  CHR10P118975684 25 0.975 90.00% 9 of 10 70.00% 7 of 10CHR11P021861414 26 6 90.00% 9 of 10 80.00% 8 of 10 CHR12P004359362 272.985 50.00% 5 of 10 90.00% 9 of 10 CHR12P016001231 28 1.21 100.00% 9 of9  20.00% 2 of 10 CHR14P018893344 29 1.21 70.00% 7 of 10 80.00% 8 of 10CHR14P093230340 30 1.84 60.00% 6 of 10 90.00% 9 of 10 CHR16P000373719 310.965 66.67% 6 of 9  77.78% 7 of 9  CHR16P066389027 32 2.62 80.00% 8 of10 80.00% 8 of 10 CHR16P083319654 33 2.845 100.00% 10 of 10  80.00% 8 of10 CHR18P019705147 34 5.775 10.00% 1 of 10 100.00% 10 of 10 CHR19P018622408 35 2.425 80.00% 8 of 10 80.00% 8 of 10 CHR19P05189282336 1.585 75.00% 6 of 8  100.00% 6 of 6  CHRXP013196410 37 1.445 50.00% 5of 10 80.00% 8 of 10 CHRXP013196870 38 1.93 30.00% 3 of 10 90.00% 9 of10 ha1p16_00179_l50 39 1.055 100.00% 10 of 10  80.00% 8 of 10ha1p16_00182_l50 40 0.645 100.00% 10 of 10  80.00% 8 of 10ha1p16_00257_l50 41 0.515 88.89% 8 of 9  70.00% 7 of 10 ha1p_12601_l5042 0.66 80.00% 8 of 10 40.00% 4 of 10 ha1p_17147_l50 43 0.61 80.00% 8 of10 40.00% 4 of 10 ha1p_42350_l50 44 3.685 70.00% 7 of 10 77.78% 7 of 9 ha1p_44897_l50 45 3.565 80.00% 8 of 10 90.00% 9 of 10 ha1p_61253_l50 461.785 70.00% 7 of 10 55.56% 5 of 9  CHR01P001005050 47 1.4 50.00% 4 of8  75.00% 6 of 8  CHR16P001157479 48 6 100.00% 2 of 2  0.00% 0 of 2 ha1g_00681 49 1.11 100.00% 10 of 10  80.00% 8 of 10 ha1g_01966 50 2.2270.00% 7 of 10 90.00% 9 of 10 ha1g_02153 51 0.59 90.00% 9 of 10 80.00% 8of 10 ha1g_02319 52 0.53 70.00% 7 of 10 70.00% 7 of 10 ha1g_02335 531.61 90.00% 9 of 10 80.00% 8 of 10 ha1p16_00182 54 0.67 100.00% 10 of10  80.00% 8 of 10 ha1p16_00185 55 0.75 90.00% 9 of 10 80.00% 8 of 10ha1p16_00193 56 2.12 80.00% 8 of 10 100.00% 10 of 10  ha1p16_00259 571.23 100.00% 10 of 10  70.00% 7 of 10 ha1p_02799 58 3.75 55.56% 5 of 9 70.00% 7 of 10 ha1p_03567 59 2.52 66.67% 6 of 9  70.00% 7 of 10ha1p_03671 60 1.21 20.00% 2 of 10 100.00% 10 of 10  ha1p_05803 61 2.13100.00% 10 of 10  50.00% 5 of 10 ha1p_07131 62 6 80.00% 8 of 10 90.00% 9of 10 ha1p_07989 63 4.73 80.00% 8 of 10 80.00% 8 of 10 ha1p_08588 645.31 30.00% 3 of 10 100.00% 10 of 10  ha1p_09700 65 0.77 11.11% 1 of 9 100.00% 9 of 9  ha1p_104458 66 4.53 50.00% 5 of 10 80.00% 8 of 10ha1p_105287 67 3.69 50.00% 5 of 10 80.00% 8 of 10 ha1p_10702 68 0.5740.00% 4 of 10 90.00% 9 of 10 ha1p_108469 69 1.02 50.00% 5 of 10 77.78%7 of 9  ha1p_108849 70 3.36 50.00% 5 of 10 100.00% 9 of 9  ha1p_11016 714.1 50.00% 5 of 10 100.00% 9 of 9  ha1p_11023 72 2.64 66.67% 6 of 9 55.56% 5 of 9  ha1p_12974 73 1.5 40.00% 4 of 10 100.00% 10 of 10 ha1p_16027 74 1.27 80.00% 8 of 10 100.00% 10 of 10  ha1p_16066 75 1.266.67% 6 of 9  100.00% 9 of 9  ha1p_18911 76 3.67 40.00% 4 of 10 80.00%8 of 10 ha1p_19254 77 3.55 90.00% 9 of 10 60.00% 6 of 10 ha1p_19853 780.5 90.00% 9 of 10 50.00% 5 of 10 ha1p_22257 79 1.29 50.00% 5 of 1080.00% 8 of 10 ha1p_22519 80 2.52 40.00% 4 of 10 100.00% 10 of 10 ha1p_31800 81 3.25 55.56% 5 of 9  90.00% 9 of 10 ha1p_33290 82 1.6844.44% 4 of 9  77.78% 7 of 9  ha1p_37635 83 4.19 80.00% 8 of 10 50.00% 5of 10 ha1p_39189 84 0.9 44.44% 4 of 9  80.00% 8 of 10 ha1p_39511 85 0.7680.00% 8 of 10 50.00% 5 of 10 ha1p_39752 86 3.14 80.00% 8 of 10 40.00% 4of 10 ha1p_60945 87 1.9 70.00% 7 of 10 60.00% 6 of 10 ha1p_62183 88 4.5360.00% 6 of 10 100.00% 10 of 10  ha1p_69418 89 5.38 90.00% 9 of 10100.00% 10 of 10  ha1p_71224 90 0.91 90.00% 9 of 10 60.00% 6 of 10ha1p_74221 91 1.58 100.00% 10 of 10  60.00% 6 of 10 ha1p_76289 92 1.2940.00% 4 of 10 90.00% 9 of 10 ha1p_81050 93 5.86 70.00% 7 of 10 100.00%10 of 10  ha1p_81674 94 1.36 70.00% 7 of 10 80.00% 8 of 10 ha1p_86355 950.9 70.00% 7 of 10 80.00% 8 of 10 ha1p_98491 96 5.45 50.00% 5 of 1090.00% 9 of 10 ha1p_99426 97 0.66 60.00% 6 of 10 80.00% 8 of 10Threshold: Average dCt value established by ROC curve analysis asoptimal threshold for distinguishing tumor and adjacent normal tissues.Sensitivity: % of positive (i.e., methylation score above Threshold forgain of methylation markers or below Threshold for loss of methylationmarkers) tumors. Pos. of Total: Number of positive tumors relative tothe total number of tumors analyzed. Specificity: % of negative (i.e.,methylation score below Threshold for gain of methylation markers orabove Threshold for loss of methylation markers) adjacent normalsamples. Neg. of Total: Number of negative adjacent normal samplesrelative to the total number of adjacent normal samples analyzed.

Although the invention has been described in some detail by way ofillustration and example for purposes of clarity of understanding, itwill be readily apparent to one of ordinary skill in the art in light ofthe teachings of this invention that certain changes and modificationsmay be made thereto without departing from the spirit or scope of theappended claims.

All publications, databases, Genbank sequences, patents, and patentapplications cited in this specification are herein incorporated byreference as if each was specifically and individually indicated to beincorporated by reference.

1. A method for determining the presence or absence of ovarian cancer inan individual, the method comprising: a) determining the methylationstatus of at least one cytosine within a DNA region in a sample from theindividual where the DNA region is at least 90% identical to a sequenceselected from the group consisting of SEQ ID NO: 436, 389, 390, 391,392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405,406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419,420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433,434, 435, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448,449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462,463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476,477, 478, 479, 480, 481, 482, 483, 484, and 485; b) comparing themethylation status of the at least one cytosine to a threshold value forthe at least one cytosine, wherein the threshold value distinguishesbetween individuals with and without ovarian cancer, wherein thecomparison of the methylation status to the threshold value ispredictive of the presence or absence of ovarian cancer in theindividual.
 2. The method of claim 1, wherein the determining stepcomprises determining the methylation status of at least one cytosine inthe DNA region corresponding to a nucleotide in a biomarker, wherein thebiomarker is at least 90% identical to a sequence selected from thegroup consisting of SEQ ID NO: 292, 293, 294, 295, 296, 297, 298, 299,300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313,314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327,328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341,342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355,356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369,370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383,384, 385, 386, 387, and
 388. 3. The method of claim 2, wherein thedetermining step comprises determining the methylation status of the DNAregion corresponding to the biomarker.
 4. The method of claim 1, whereinthe sample is from blood serum, blood plasma, or a biopsy.
 5. The methodof claim 1, wherein the methylation status of at least one biomarkerfrom the list is compared to the methylation value of a control locus.6. The method of claim 5, wherein the control locus is an endogenouscontrol.
 7. The method of claim 5, wherein the control locus is anexogenous control.
 8. The method of claim 1, wherein the determiningstep comprises determining the methylation status of at least onecytosine from at least two DNA regions.
 9. A computer-implemented methodfor determining the presence or absence of ovarian cancer in anindividual, the method comprising: receiving, at a host computer, amethylation value representing the methylation status of at least onecytosine within a DNA region in a sample from the individual where theDNA region is at least 90% identical to a sequence selected from thegroup consisting of SEQ ID NO: 436, 389, 390, 391, 392, 393, 394, 395,396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409,410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423,424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438,439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452,453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466,467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480,481, 482, 483, 484, and 485; and comparing, in the host computer, themethylation value to a threshold value, wherein the threshold valuedistinguishes between individuals with and without ovarian cancer,wherein the comparison of the methylation value to the threshold valueis predictive of the presence or absence of ovarian cancer in theindividual.
 10. The method of claim 9, wherein the receiving stepcomprises receiving at least two methylation values, the two methylationvalues representing the methylation status of at least one cytosinebiomarkers from two different DNA regions; and the comparing stepcomprises comparing the methylation values to one or more thresholdvalue(s) wherein the threshold value distinguishes between individualswith and without ovarian cancer, wherein the comparison of themethylation value to the threshold value is predictive of the presenceor absence of ovarian cancer in the individual.
 11. A computer programproduct for determining the presence or absence of ovarian cancer in anindividual, the computer readable product comprising: a computerreadable medium encoded with program code, the program code including:program code for receiving a methylation value representing themethylation status of at least one cytosine within a DNA region in asample from the individual where the DNA region is at least 90%identical to a sequence selected from the group consisting of SEQ ID NO:436, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401,402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415,416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429,430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 441, 442, 443, 444,445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458,459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472,473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, and 485; andprogram code for comparing the methylation value to a threshold value,wherein the threshold value distinguishes between individuals with andwithout ovarian cancer, wherein the comparison of the methylation valueto the threshold value is predictive of the presence or absence ofovarian cancer in the individual.